Student Profiles

Currently, with the growing concern in adopting more sustainable technical solutions, the interest in earth as a building material has been renewed. The present proposal has as main objective the development of compressed earth blocks (CEBs) capable of meeting the requirements of safety, durability, comfort and hygiene proportional to the expectations of the modern world, contributing to the circular economy. Thus, it is expected to optimize the thermal behavior of the CEBs from the incorporation of low-cost natural materials and to study the incorporation of construction and demolition waste (CDW), without neglecting the optimization of its mechanical and structural performance. The desired products will represent more economical solutions, with reduced environmental impact, in a perspective of contribution to sustainable development. The results of the study will assist professionals who intervene in the rehabilitation and conception of new buildings using earthen construction.

The Intergovernmental Panel on Climate Change urges industries to disrupt with changes. From design to build takes years, and decades to operate, making it a long-lasting machine that has a large impact on the climate and biodiversity and could be thought to regenerate the environment. The International Style, developed at the beginning of the 20th-century, paid no interest to climate or past knowledge. The digital turn in architectural design — computer simulation and performance optimization allows not only creating ideas and processes but also revisiting vernacular, historical, early-modern and counter-culture architecture and with accurate tools in the search for solutions for better daylight conditions, energy consumption or ventilation. This proposal looks for regenerative architecture schemes in three domains (Energy, Materials and Health) for rapidly changing: retrofitting and urbanizing, regions and those researching borders concepts like tradition/innovation, local/innovation to create a sustainable built environment. Finally, the proposal is in line with the New European Bauhaus and its local version the NEB Goes South that has been developed at the University of Porto and partners recently .

The project aims the development of (i) data acquisition strategies for waste management systems, using the northeast of Portugal as a model region, and (ii) innovative technological solutions to valorise solid waste streams into carbon nanostructured materials (CNMs), with applicability in the treatment of leachate waters. Learning algorithms will be applied to characterize the waste, and optimization algorithms will be used to optimize waste transportation considering the consumer’s behaviour. Regarding waste valorization, CNMs will be prepared from plastic solid wastes by chemical vapor deposition. These as-synthesized CNMs will be employed as precursors to produce catalysts and membranes for advanced oxidation and separation technologies to treat landfill leachates. The project will be conducted in collaboration with a waste management company (Resíduos do Nordeste) dealing with the municipal solid waste generated in the northeast region of Portugal.

The bioclimatic solutions of vernacular architecture and its principles of adapting the building to the climate constitute important lessons in constructive and environmental sustainability. Thus there is a strong correlation between construction and morphological characteristics and the respective local climatic contexts.

Contemporary architecture ends up neglecting these issues to a certain extent, where traditional and empirical knowledge has been lost over time, as well as cultural identity, giving rise so often to constructions disconnected from the characteristics of the “region”, and without sustainable basis in constructive and environmental terms.

The PhD work plan intends to reflect on the advantages of recovering the empirical knowledge of vernacular construction, being able to serve as a guideline in the eventual creation of specific regulations. These guidelines are also suitable for application in countries with similar climatic characteristics, highlighting the traditional materials available locally and the techniques used in construction, together with the possibility of linking these constructive solutions to the contemporary technologies available.

The objective of this study is to demonstrate the potential of the sludge-treatment-reed-bed (STRB) assisted with earthworm as a sustainable solution for sewage sludge management within a smart city plan. This alternative solution can be used instead of conventional ones that are costly, low in life expectancy and by-products need further treatment. This cost-effective project will be developed in cooperation with the water industry (Frielas wastewater-treatment plant) that will supply sludge samples and validate the benefits of this innovative technology. The project comprises earthworms’ roles in sustainable sewage sludge management, dewatering and stabilization processes, evapotranspiration effect in the dewatering and the assessment of the operational requirements for STRB technology to develop a technical and economic evaluation analysis. The knowledge gathered from this research will contribute to minimize environmental impacts of climate changes as well.

Modern vehicles are capable of sharing information (traffic conditions, entertainment services, and more) between other vehicles on the road. Because of this possibility, there are safety breaches that can put the vehicle and drivers/passengers at risk. Our work is focused on developing an intrusion detection system to increase security levels.

Two of the main issues big cities face are clean and safe water supply and efficient municipal solid waste (MSW) management. As a new vision, MSWs can be valorized by transforming them into high added value products, such as geopolymer membranes (GPMs) for water treatment (WT) applications. Geopolymers are aluminosilicates forming long-range, covalently bonded networks that have been largely explored as concrete replacers, but they may be molded to acquire any geometric shape, such as cylindrical membranes. This research will focus on fabrication approaches of GPMs from matured compost obtained by biological and composting treatments of the organic fraction of MSWs for the development of clean technologies for solid waste management (SWM) in sustainable cities. For the preparation of GPMs, different additives, precursor ratios, and mature conditions will be used to tune the properties of GPMs and yield excellent rates in WT. Life-cycle assessment will also be evaluated.

Seismic oceanography has proven its value to imaging the ocean in high-resolution using conventional multichannel reflection seismic data (MCS). The recorded acoustic responses depend on contrasts in ocean temperature and salinity. The processed data enable tracking interfaces between thermohaline layers and the study of fine-scale processes spatially. However, their interpretation has been focused on qualitative methods, and integration with dynamic ocean models is still unexplored. In this PhD, I will develop and implement methods to predict spatially ocean temperature and salinity through the integration of MCS data with direct observations (e.g., CTD, ARGO) in a spatial data science framework. These methods will be applied to existing data from Gulf of Cadiz. As a complementary and exploratory approach, and since MCS data acquisition is complex and expensive, I will look at using high-resolution echosounders mounted in autonomous underwater vehicles to model in real-time the ocean.

The aim of PhD project is to conceive improved bone implants and prosthesis based on the gyroid structure. Gyroid foams allow to obtain suitable mechanical properties to mimic human bone contributing to the performance of the implant in the patient. To do so, innovative tools such as machine learning will be used. Moreover, it is important to mention that the gyroid structure can be used for structural purposes, and thus, build lighter yet structurally sound parts.

Water distribution systems (WDS) are energy-intensive infrastructures that rely on water storage plants and energy for delivering water to consumers. The combination of three complementary trends offer water companies opportunities for a novel positioning in the energy sector: the flexibility in scheduling pumping operations leading to implementing demand response (DR) strategies with benefits to water and energy utilities, current investments in renewable energy sources (RES), new dynamic pricing (DP) tariffs in smart grids. Therefore, this research aims to develop novel models and algorithms to cope with the increased complexity of real-time optimization of energy resources in WDS operation encompassing the management of DR actions, energy availability from RES, time-differentiated tariffs and participation in emerging energy markets. Mathematical programming models will be developed accounting for the network infrastructure of water and energy sectors, exploiting their synergies.

The transition from fossil fuels to renewable energy sources is a strategic goal in many countries, driven by economic concerns and the imperative to address climate change. However, land-use conflicts associated with the increase of renewable energy production sites have led to the loss of natural and semi-natural areas, casting doubt on the sustainability of this infrastructure deployment. Additionally, there is also a concern to increase efficiency of energy consumption, namely urban areas and their morphology. Therefore, this PhD project aims (i) to assess the impacts resulting from the implementation of the renewable energy production infrastructures; (ii) to analyse the connection between urban form and renewable energy consumption patterns; and (iii) to develop various spatial scenarios for 2050 to optimize land use in aligning urban form with renewable energy consumption patterns. The achieved results may help decision-makers to create more effective energy transition policies and integrating energy considerations into spatial planning, namely through land management instruments.

The potential of Human-Robot Collaboration (HRC) is far from exhausted. Currently, Collaborative robots (COBOTs) are advertised for ease of commissioning and the capacity to operate without fencing, and thus share the workspace with humans. Even when the COBOT is programmed to admit some degree of interaction with the worker, the robot system is “socially blind” to Ergonomics & Human Factors (E&HF). The lack of a valid and sound ergonomics framework cognizant of the implications of HRC, as well as the inexistence of a correlation between risk assessment/prevention and complementary robot behaviors, hinders the progress in this direction. To evolve the state of the art of current collaborative robots in the manufacturing industry, this project propose the creation of a real-time assessment framework appropriate to the HRC tasks. This expected outcome will support the next step towards collaborative robots capable of understanding and adapting to improve the physical and cognitive ergonomics of human coworkers.

Although climate change impacts on agricultural crops in Portugal have already been extensively studied, research is still lacking on water availability for agriculture, as well as on its irrigation demands, under a climate change future. Therefore, the goal of my PhD project is to predict the future crop irrigation needs and agricultural water availability in Portuguese watersheds, using a coupled crop-watershed modelling system forced by state-of-the-art regional climate data. The outcomes of my research will be used to define climate change adaption strategies for the agricultural sector, that will be made available to farmers and other agriculture professionals through a web-based platform.

In the context of ongoing climate change, awareness about transport infrastructure exposure to extreme events has been increasing. Likewise, the urgency of climate adaptation is also at the top of priorities, particularly in developed countries that have large transport networks to preserve. The main efforts conducted in the past have focused on the development of advanced models to tackle single hazards affecting one or two transport assets. While recognizing the importance of such efforts, a way to analyze several consecutive hazards affecting several assets simultaneously is still missing. Therefore, reality is actually what isn’t being simulated. This thesis proposes a hierarchical methodology to create the most probable scenarios, the vulnerability of the transport assets affected by each scenario, as well as the consequences that their failure may introduce into the transport network functionality. The results will contribute to the resilience of the transport network and decision-making improvements.

Developing models to simulate the biological results of hydromorphological rehabilitation on urban streams.

The Industry 4.0 revolution has grown the necessity of the Facility Management (FM) discipline to deal with live data. Therefore, the Digital Twin (DT) technology can significantly support the FM sector and its management processes. DTs can be essential for operating “dynamic facilities” ,understood as those undergoing recurrent changes in their layout. For the BIM models as the primary source of information and graphical representation in a DT, open formats such as IFC are encouraged (open BIM). However, because of the high complexity of the IFC schema, the object-based real-time layout modification of IFC DTs is a challenge to overcome. In this context, FM lacks guidance on implementing an efficient integrated management system necessary for the current industry demands. Therefore, this work proposes an approach to implementing a new integrated management system and the method to guarantee a full DT to reflect real-time objects’ location using the IFC schema.

The use of ‘Big’ unstructured data to assess and monitor the distribution of the world’s species (i.e., ‘iBiogeography’) is in its infancy. Here I aim to expand this field of research to improve existing knowledge about the geographical patterns and trends of the ‘Culicidae’ family (i.e., mosquitoes), a taxonomic group of particular concern for disease transmission. To assess this, I will firstly work on the development of a ‘deep learning’ algorithm that will allow classifying and process (check)lists of species, that are published in high numbers by the scientific literature, each year. Secondly, I will develop and test a Web content mining approach to recurrently searching information items (e.g., images; social media content) evidencing range shifts of problematic species. Finally, I will use the biogeographical knowledge to gather through the two previous tasks, and test how the distribution of culicids has already reshaped their distributions in response to global climate change.

The research work aims to propose a holistic methodology for the structural integrity preservation of Built Cultural Heritage (BCH) to be framed in the broader scope of a Digital Twin (DT) paradigm implementation. The design of new DT processes and technologies for buildings requires holistic thinking. Therefore, a multidisciplinary framework will be proposed to make the BCH structures’ conservation process less expensive, more efficient and more reliable than existing approaches.

The theme of sustainability of cities is a very current and interdisciplinary topic that leads to a series of interests and influences. Urban sustainability has a clear importance and interest in tourists/visitors, host populations, tourism companies and local/national governments. Tourism, as a type of mobility, leisure, practice, economic, cultural, social phenomenon, can directly and/or indirectly promote the sustainability of cities, therefore, it is proposed to explore and verify the sustainability of portuguese cities for tourism. As national case studies the objective is to present the main cities in Portugal – Porto, Coimbra, Lisbon, Faro, Ponta Delgada and Funchal. By reading and analysing various national and international sources of information, creating surveys, questionnaires, making specific visits/travels, studying good international practices, it will be possible to present strategies for improving and/or creating greater sustainability in the selected cities.

The urge of reverting climate change has made sustainability assessment a mandatory requirement within the AEC industry. The adoption of sustainable parameters by housing design becomes inevitable.
However, in affordable housing, the investment in sustainability is quite restricted due to the highly constrained budget limitations. Furthermore, Lifecycle assessment (LCA) adds difficulties to many conceptual aspects of affordable housing because by definition it considers sustainability over the entire lifecycle of the building (from construction to operation, maintenance, demolition, and materials reuse or recycling).
Digitalization in the AEC industry and the use of the data in digital format creates opportunities for collection and analysis of data that may help LCA and provide information on which solutions provide better sustainability performance, hence guiding design towards more optimized solutions.
This research aims at using Data-driven techniques including machine learning and based on BIM models develop tools for optimizing design decisions that improve LCA results.

As a disruptive technology, additive manufacturing (AM) is changing the state and structural dynamics of the supply chain (SCs), and their ability to cope with SC vulnerabilities and disruptions, i.e., supply chain resilience (SCR). SCR is vital for business continuity when facing unforeseen disruptions such as the recent COVID-19 pandemic. However, to date, no research has investigated the implications of AM adoption for SCR, and therefore, this PhD project aims to fill this knowledge gap. Drawing on a mixed-method research approach, this PhD project will investigate how AM adoption impacts the state of SCs that consequently influence certain SC capabilities and vulnerabilities, thereby affecting SCR. Subsequently, an AM-SCR framework will be developed to aid decision-makers in the optimal (re)design of SCs by determining which resilience practices to capitalize on when using AM technology so as to develop the necessary SC capabilities to deal with SC vulnerabilities more effectively.

Worldwide, climate change is critically affecting marine environments, particularly estuaries, through ocean warming, sea level rise, extreme events and acidification. Despite the global evidence, regional studies focused on this subject are still in need, especially on the Western Iberian Coast, as there is a lack of long-term temporal and spatial analysis assessing the evidence of climate change effects in an integrated study of this region. The main aim of this project is, therefore, to search for regional evidence of climate change considering estuaries and adjacent coastal ocean, using in situ observations and satellite imagery. Future scenarios will be assessed for different estuarine systems through the application of a numerical model, that will allow understanding the regional and local impacts of climate change in the Western Iberian Coast.

Modern vehicles are capable of sharing information (traffic conditions, entertainment services, and more) between other vehicles on the road. Because of this possibility, there are safety breaches that can put the vehicle and drivers/passengers at risk. Our work is focused on developing an intrusion detection system to increase security levels.

The use of fossil fuels is directly related to environmental impacts concerning atmospheric emissions. So, many strategies have been discussed to the development of sustainable energy process based in renewable sources, aiming at a zero-residue chain. The ALG-TERNATIVE project aims at developing a low carbon, simple, technically feasible, inexpensive, environmentally friendly, and replicable process for biomass conversion into biofuels. Urban wastewater will be used as a feedstock for microalgae cultivation, which will be directly converted into drop-in liquid biofuels and bio-based products through Hydrothermal Liquefaction (HTL). The main advantage of HTL process is that there is no need to dry the biomass slurry prior the thermochemical conversion pathway, saving energy and costs. An upgrading of bio-oil will be carried out in order to delivery a product as close as possible to petroleum fuels or biodiesel. A significant global reduction in green house gases (GHG) emissions is expected as well as cleaned water which can be reused or discharged in compliance with the current EU legislation. Profits obtained from theWW treatment process, low cost feedstock and carbon credits will allow a reduction in production costs. Is is expected that this research can be replicated with different types of wastewater and in different locations within the EU.

The increasing dispersion of microplastics in the aquatic environment has been the subject of concern for the consequences of their inclusion in ecosystems and public health.
Currently, the main alternatives for identifying microplastics, are the Raman and FTIR. These are very reliable methods for laboratory analysis of microplastics, however, they involve expensive and massive equipment that requires sample collection and pre-processing.
This work aims to revolutionize the microplastic analysis paradigm by proposing a sensor for in-situ analysis, using previously developed lab-on-a-chip (LoC) devices and develop on it photoacoustic and infrared detection techniques, to achieve an accurate, autonomous, low-cost, and easy to operate microplastics sensor. This will allow the construction of a multisensor platform for massive placement in an underwater environment. Massive monitoring for big data analysis will allow a significant advance in the control of pollution of marine ecosystems.

The proposed research program is a part of the ERC research project STAND4HERITAGE and aims to assess the seismic behaviour of masonry structures. More specifically, the STAND4HERITAGE project faces the ambitious challenge of introducing new standards for safeguarding built cultural heritage for the next generations and developing approaches able to predict the seismic response of masonry systems. In this context, out-of-plane mechanisms have been identified as the principal responsible for disastrous scenarios and the associated dynamic phenomena are still requiring further investigation. The grand objectives of the project address the 11th goal of the UN 2030 Agenda, which deals with the Sustainability of Cities and Communities. The achievement of the established target is organised in four work packages: 1) WP1-Study of the seismic input signal, 2) WP2- Data sets of experimental dynamic responses of masonry structures, 3) WP3-Numerical approaches for simulation of out-of-plane behaviour and 4) WP4-Analytical approaches for the out-of-plane assessment. The present PhD doctoral research program focuses on the development of the second work package, which deals with the performance of numerous experimental tests on masonry structures. The project focuses on the characterisation of the static and the dynamic out-of-plane behaviour. The two main objectives of the present research project are to provide experimental data to improve the knowledge of non-well known phenomena of masonry dynamics and identify the key parameters that significantly influence the out-of-plane response.

Cycling is increasingly recognized as a fundamental element in urban mobility, and municipalities need new strategies to make better-informed decisions and investments.
In recent years, there have been a growing volume of cycling data, and new mobility models have emerged trying to assess the cycling ecosystem from many perspectives. However, no model has become widely accepted to supporting decision-making. This seems mainly due to their development paradigm, which is usually a self-contained process, making these models difficult to generalize, reproduce and repurpose to other scenarios or datasets, if not at all impossible
The objective of this work is to propose a new mobility model development paradigm, focused on incremental innovation, where new elements (models, visualizations, transformations, …) are developed focusing on integrating and complementing existing ones.
The second is the development of an instantiation of a mobility platform for supporting the incremental development and comparison of mobility models.

The plastic industry plays an essential role in improving the quality of life for millions of people worldwide. However, the growing demand for plastic products has increased the pressure to find more sustainable solutions. Therefore, it is necessary to integrate environmental aspects into product design and development, along with economic and technical requirements, to support the sustainable production of plastic products. This research project aims to develop a novel ecodesign approach using applications from the plastic industry to improve their life-cycle performance. Three plastics applications will be analysed: nutraceutical bottle, automotive interior door handle and carrier frame for instrument cluster. The project involves exploring different ecodesign strategies, such as incorporating alternative materials. The novel ecodesign approach will integrate methodologies, such as Life Cycle Assessment & Costing, with Multi-Criteria Decision Analysis and Uncertainty analysis. The Stochastic Multi-Attribute Analysis is being explored to compare the environmental and other criteria and evaluate different weighting preferences (or decision makers with conflicting priorities). The outcome is to deliver a novel ecodesign approach that integrates complementary methodologies to provide robust decision support for the development of more sustainable plastic products.

Coastal areas are complex environments which face extreme challenges regarding their resilience. Over the years, eroding coastlines have mostly been protected by hard engineering structures (groynes, jetties, breakwaters). These, however, lead to sedimentary imbalance, hence the growing popularity of combining them with Nature-based Solutions – such as saltmarshes, mangroves, bivalve reefs, and coral reefs -, which enhance ecosystem services. My researcg work will analyse the possible implementation of oyster reefs in Ria Formosa, which improve water quality, while also dissipating wave energy and protecting the coast. To assess this possibility, it is first important to monitor the erosion problem in the area, through remote sensing techniques (drone and satellite imagery). Addiionally, the different existing protection strategies in Ria Formosa will be compared, regarding the opinion of regional stakeholders on their benefits and cost-effectiveness, using a multicriteria analysis (Analytic Hierarchy Process). The results will be fundamental for the application of a Cost-Benefit Analysis to determine the most cost-efficient and sutainable coastal protection alternative.

To address the climate change problems, many global cities and international organisations have introduced various policies and guidelines to promote sustainability. The aim is to find ways to accelerate transitions to strengthen the resilience of cities and achieve sustainability. In documents and reports, transport and mobility are cited as important elements in the transition and reducing greenhouse gas emissions. However, the problems of urban unsustainability are persistent, and there is urgent talk about how to accelerate sustainability transitions. For a transition to take place, it is necessary to bring about changes in ways of doing things (practices), ways of thinking (culture) and ways of organising (structures), which unfold in different phases. This study draws on two theoretical and practical frameworks for analysing sustainable transitions: the multilevel perspective (MLP) and socio-practice theory (SPT). MLP has progressed in recent years in understanding the multidimensionality of socio-technical systems, niche-regime interactions and the more differentiated relationships between new participants and incumbents. SPT is a sociological theory that analyses the connection between individual human actions and the social world, explaining social and cultural phenomena. Practices are built on images (meanings and symbols), skills (know-how) and materials (artefacts and technologies). So far, academic research on sustainability transitions has tended to focus on state and market actors, leaving aside the possible role that members of civil society can play in these processes. Many scholars consider public participation to be a prerequisite for sustainability transitions. Therefore, this research focuses on analysing the role of social movements in sustainable transitions in urban mobility, namely in the starter cycling cities in the global North and South.

Commonly, cities are organized around areas that present different socio-economic dynamics. Understanding these dynamics is of main interest to evolve and promote urban livability and sustainability. Mobile operators collect data with geographic references. Such data can play a crucial role in inferring human mobility and communication patterns. Moreover, given the worldwide availability of mobile phones, the data collected through them can represent all groups of the population.
The aim of this research is to model how people move and socialize in a city through the analysis of spatial data, with special attention to understanding how citizens use the public space. We use the semantics of the different areas including information on socio-economic behavior, crimes, and Points of Interest (POIs) to identify areas of the city with distinct dynamics. Mobile phone data is also analyzed to perceive these dynamics and recognize the mobility and homophily patterns of the population. Such information can be used to understand the movement flows, identify how the space is used, and if there are potentially isolated zones.
Ultimately, the identification of vulnerable areas in public spaces can be used to promote safety and inclusiveness actions. This information is valuable for decision-making, in order to prevent crime, avoid potential isolated communities, and generally, improve the quality of living in urban areas. Additionally, the recognition of citizens’ mobility patterns can be used to improve public transport and, therefore, reduce traffic congestion and environmental pollution, which are consequences of accelerated urbanization.

In response to the climate emergency and the Sustainable Development Goals for 2030, it is important to reduce CO2 emissions and make buildings more sustainable and energy efficient. The development of a prefabricated wood-based system emerges as an innovative solution to optimize the rehabilitation of reinforced concrete buildings, contributing to a more resilient and eco-friendly built environment. The main goal of this system is to create a new exterior envelope that improves energy efficiency and structural safety, while incorporating new spaces and uses into the interior of the dwellings. Additionally, it is possible to renovate the architectural image of the building in a low disruptive manner, as this intervention is carried out from the exterior. Our focus is on post-1960s reinforced concrete (RC) structures, which represent the majority of the Portuguese building stock.

Angola presents an electrification rate of 43% corresponding to 18 million people without electricity access and has defined the expansion of the power system mainly with hydropower plants because there is a huge potential estimated at more than 18 GW. However, this dependence does not ensure the reliability of the power system in drought periods, being important the integration of the other non-conventional power plants like renewables energies sources (RES) also with high potential. The present research proposal aims to develop a power generation expansion planning model with large-scale RES integration for developing countries. The case of study will be the Angolan power sector. The model will take into account aspects such as the costs of expanding the electrical grid, the integration of intermittent RES into the electrical grid, the complementarity between RES and regions, distributed generation, and storage options. It will provide solutions to help power sector decision-makers.

Recently, there has been a growth of timber buildings driven by the development of Engineered Wood Products (EWP) like Cross-Laminated Timber (CLT) and increasing concerns about sustainable development. However, the durability of these products in various climates is still either questionable or unknown. Therefore, this study will quantify the predictable reduction, due to biological deterioration, in the service life of CLT under Use Class 2 exposure conditions, considering different climates in Portugal. Monitoring systems, wetting/drying tests, accelerated biological deterioration tests followed by mechanical tests, Finite Elements Models, and decay numerical models will be developed and applied. Also, climate change effects on the results will be assessed using future climates scenarios. Based on this analysis, preventive measures, and maintenance plans to improve the service life will be defined as Guidelines for EWP durability.

Solar thermal energy systems have been considered a promising solution to achieve carbon neutrality by 2050. Concentrated solar thermal (CST) technologies collect and concentrate solar radiation to transform it into high-temperature thermal applications, such as process heat or electricity production. However, it is necessary to enhance the efficiency of CST technologies and use low-cost manufacturing processes to make them attractive to the international energy sector. Concentration Solar Power (CSP) is recognized as a renewable energy technology that can make a sounder contribution to the transformation of the energy sector since it captures and stores the sun’s radiation in the form of heat, using stable materials. From the different CSP technologies, the parabolic dish solar concentrator (PDSC) is considered one of the most promising solutions since it can be applied in several applications from the electricity production to the cooking, desalination, and irrigation. This versatility makes PDSC suitable to be implemented in regions in which the access to electricity is very scarce, improving the quality of life of rural population. This system composed of a solar concentrator, solar receiver, heat exchange fluid, and the solar tracking system, ensures high concentration ratio being appropriate for high temperature applications. Power generation through the solar thermal route relies on an efficient solar receiver that uses the incoming energy to produce electricity or thermal energy by heating a fluid. However, other components directly influence the efficiency of the system, the solar collector collects solar radiation and reflects it to the solar receiver, the material, size, angle of inclination, and distance from the receiver influence the efficiency of the system. The working fluid exchanges heat with the receiver, playing an important role on the heat transfer performance of the system. The solar tracker automatically follows the movement of the sun, ensuring that the solar collector always receives the highest intensity of solar radiation. Due to the complexity of this PDSC, the costs of materials and manufacturing technology are huge, making this technology expensive. To make this technology more effective and attractive to the market, there is a need to increase its efficiency and invest in new materials and manufacturing technologies at affordable costs. In this context, to provide an innovative solution that contributes to the clean transformation of the energy sector, this thesis proposal aims to develop an efficient PDSC technology using multiple impact jets and porous media to increase solar receiver efficiency, apply advanced manufacturing technologies, and materials sustainable to obtain an economical and reliable system. To conduct the project, experimental and numerical methods will be applied. This work presents the motivation and the literature review that supports the objectives and the research opportunity of this proposal. The methodologies and the different tasks that are expected to be conducted to achieve the project goals will be presented in this work plan.

UV-filters are emerging pollutants related with the massively use of human care products. Little is known about their levels and humans and ecological risks. Ocean-cities are relevant todevelop solutions to emerging urban problems in view to sustainable progress. This work will be carried out in Madeira Island, that has the ideal characteristics for the study of the UV-filters traceability due to its geographical isolation, demography and geomorphology that allow interactions between marine environments, from the coast to deep water systems, human activities, and waste waters. The presence of these contaminants in coastal systems will be studied to observe their bioaccumulation and biomagnification along food chains in different habitats, from primary to apex consumers. Ecological sustainable solutions to decrease the UV-filters environmental risk will be inferred. The scientific knowledge obtained can changeattitudes and behavior towards a more sustainable life.

Modern power systems are characterized by an increasing need of Ancillary Services (AS) due to forecasting errors, unpredicted contingencies in the internal system and unforeseen disturbances in neighboring systems. After decades of independent evolution of transmission and distribution grids, recent research projects have been addressing TSO-DSO cooperation in terms of shared big data platforms of both networks’ real-time measurements. In this context, we propose a Model Predictive Control (MPC) for the real-time activation of Ancillary Services supporting the transmission network operation, allowing for the exploitation of resources present on the distribution side. The model is intended for use either post fault or when online security assessment detects an insecurity state, and guarantees that the activation of resources does not violate the distribution grids constraints. The amount of data and model running time are challenges to be addressed through a linearization process. The methodology will be applied using to real world data from European networks.

The Mobility concept, as an objective to achieve the Goals defined for 2050, is the catalyst pretext to explore matters intrinsic to the domain of Architecture. The central research laboratory is Braga. However, in order to incorporate the current best practices in Sustainable Urban Mobility (SUM), five cities have been selected – Bielefeld, Oxford, Pontevedra, Barcelona and Copenhagen – which will serve as a reference to the proposed research strategy. This approach aims to explore the role of Architecture in the paradigm shift in Mobility and to evaluate the level of commitment to the goals set for 2050 and its resilience in the face of calamity situations, such as COVID-19. The potentialities to combine theory and practice are underlying the methodological principles and imbued with urban acupuncture strategies and projects (SoláMorales, 2008), that have a greater influence on changing social behaviors for SUM and on the replication capacity.

Autonomous driving has become one the evolutionary trends of the 21st century as industry races towards the realisation of the technology in its full potential. Novelties in the area of deep learning have enforced this realisation, as they demonstrate high accuracy levels in a variety of autonomy-critical tasks. Perception tasks, central to any autonomous-vision based system, have directly benefited largely from such innovations. Nevertheless, the accuracy rises being reported in literature are not necessarily followed by a rise in the confidence in these systems. The unfortunate Tesla accident, for example, happened with the propagation of a perception error throughout the pipeline of the vehicle, resulting in a collision with a white trailer that had been identified by the model as sky. The uncertainty of the classification could have been a decisive factor for the decision pipeline of Tesla’s vehicle. Even so, research efforts on perception do not generally provide uncertainty estimation for predictions, focusing only on improving dataset-dependent accuracy levels. Specific to perception, the quantification of uncertainty in different components of the model could provide information with great impact on the safety of these vehicles, yet most uncertainty estimation methodologies are generic. What is more, when available, this uncertainty can not be used in downstream tasks as the incorporation of these estimates has not been too explored in literature.

On this note, this research project aims to conceptualise uncertainty interpretations and quantification methodologies for deep-learning-based object detection for autonomous driving applications. Furthermore, it is intended to provide a case study of the use of perception uncertainty estimates in downstream layers of the AV pipeline, such as decision-making, in order to showcase whether these estimates enable error recovery (e.g. misclassifications, missing objects). We thus aim to demonstrate the importance of a statistical interpretation of deep learning models on the enhancement of the interpretability and interaction between different artificial cognitive processes, thus approximating intelligent systems and human behaviour. We believe this project has the potential to enable the increased safety and sustainability of such vehicles, by demonstrating confidence on fault tolerant behaviour, reducing accident events and preventing deaths.

This proposal comes in line with the research carried out at the University of Aveiro on the development of new liners for lightweight energy absorption systems, eco-friendly materials and biomechanics of brain injuries. Following the guidelines of UN2030 goals (namely objectives 3, 5 and 16), this work aims to provide enhancements in the domains of biomechanical injury assessment, to validate the (to be proposed) new design solutions for helmets, with a specific focus on the particular requirements of urban commuters resorting to recent micro-mobility transport solutions.

An ever-increasing number of satellites is being deployed each year, which negatively impacts the lifetime of satellites, as more debris is created and collisions occur, rendering the systems inoperative and further worsening the problem. On-Orbit Servicing (OOS) is the key to solving this issue and enabling a sustainable growth in the number of satellites. This work aims to explore hybrid and distributed approaches to allocation, guidance, and control of satellites for cooperative OOS missions. We will investigate how innovative sensors can help in estimating the motion of inoperative satellites, study formation planning strategies for multi-agent systems of satellites and develop hybrid model-based control techniques to achieve the OOS mission objectives.

The decline of water quality in coastal and transitional waters has become a worldwide concern. The increasing anthropogenic pressure on such important ecosystems highlights the importance of a continuous water quality assessment. Ocean colour remote sensing techniques are becoming widely used for synoptic monitoring of the water surface, mainly due to its low-cost and capability to provide data over extensive temporal and spatial scales. In this work it is aimed to develop improved satellite products to monitor water quality changes in transitional waters by using an innovative approach based on optical water classes, in which each pixel is classified and the most appropriate bio-optical algorithm is applied. The core of this research will be the identification of the water quality features associated with the different optical conditions and the analysis of their variability through space and time. The use of this methodology for water management applications will also be discussed.

High performance heat transfer is one of the most important needs and challenges in the aerospace industry. For example, during the last few years, the need for microelectronic cooling systems used in aerospace applications has increased enormously and efficient systems are needed. Furthermore, when it comes to space, not only is great heat removal capacity required, but also thermal insulation of components may be required at times of increased radiation exposure. In recent decades, advances in nanotechnology have led to the development of nanofluids (NFs), which are basically common fluids in which nanoparticles are added that have improved their heat transfer performance and, as a result, have become the subject of many research works. Despite the advantages, only a few nanofluids have been implemented due to the high costs to generate nanoparticles, instability and controversial thermal conductivity. The main objective of my work is to solve these challenges, reducing production costs and improving the stability of invoices. In the present project it is foreseen the development of an efficient nanothermofluid to be implemented in a refrigeration system with a minimum of mechanical components to be used in space missions.

Autonomous driving has emerged as one of the great technological promises of the 21st century, but to date, it has not yet been fulfilled. Part of the problem is due to the fact that traditional algorithms, such as finite state machines, are used to model a vast and complex domain such as autonomous driving, and other algorithms based on computational learning, which, despite their promising performance, do not allow establishing causal relationships between sensory inputs and actuation outputs. This work plan proposes to focus on the issue of establishing causal relations and explainability of a special class of neural networks, the Liquid Neural Network, and its application to the dual problem of prediction and motion planning in autonomous driving of road vehicles, by solving Partially Observable Markov Decision Processes and applying Reinforcement Learning with Safety minded Control Barrier Functions/Control Lyapunov Functions.

With the advent of autonomous vehicles (levels 3 to 5), the classic concept of the car interior must be redefined to fit the new requirements that are expected in terms of customization, comfort, and human-machine interface (HMI), adaptable interaction, and infotainment. Currently, the need to rethink the role of the car interior is a hot topic and represents several research challenges. Although there are numerous studies about autonomous car interior and human factors, there is a research gap in physical ergonomics, comfort, and cognitive overload evaluations considering the users.
The scope of this Ph.D. project is to provide criteria based on ergonomic methodologies to be integrated into innovative car interiors, adaptable to the new mobility paradigms, specifically level 4 and Level 5 autonomous vehicles interior according to ergonomic principles and evaluations.

Ecological and social problems affecting urban areas have been exacerbated by climate changes, the Covid-19 pandemic, and the Ukraine war, making the creation of strategies to increase urban resilience more pertinent and necessary than ever. From a landscape point of view, the idea of integrating food production in the concept of Green Infrastructure (Greenways) is promising, as it can offers protection for fertile lands and foster urban and periurban agriculture activities. This enriched concept also offers urban open spaces with multifunctionality, combining leisure, biodiversity improvement, water management, and food production. This research proposal aims to develop a methodology for planning Productive Urban Green Infrastructures to respond to the challenges of climate change and to provide for food security, social cohesion, and environmental justice. The final product of this work will be a strategic planning and design tool to be implemented in the Porto Metropolitan Area, both at the macro and micro scale, informed by the public and municipal stakeholders’ perception of its relevance and its estimated impact on urban resilience.

This project is focused on the development of biomimetic marine structures that support marine ecosystem restoration, coastal protection, renewable energy production or monitoring of ocean and geological dynamic systems. This sophisticated approach is based on the use of smart materials, in particular a previously developed Sustainable Bioactive Conductive Concrete (SBCC) [16,17] made especially for the marine environment. The study includes the development and characterization of a prototype of SBCC marine structures, and the study of the new paradigms and approaches that this innovation could enable, in the context of global changes. Due to its high-level characteristics, these strategies are expected to serve as future climate adaptation alternatives, providing various benefits, not only for ocean biodiversity, climate regulation and coastal protection, but also to support the implementation of ocean or geological measurement and monitoring systems, or even to produce clean energy.

Assessing and predicting structural health is one of the most important aspects of an infrastructure’s life cycle. The devices that are used in the modern era have enabled us to predict the future of the structure using various monitoring techniques and also various maintenance and management methodologies to prolong the life span of the structure and to have a healthy structure to be utilized by the user. The most contemporary system for monitoring with low installation and maintenance cost with high reliability is Micro-Electro-Mechanical Systems (MEMS) technology and Internet of Things network using LTE (4G Long Term Evolution). The management aspect can be developed using computerized systems such as Building Information Modelling (BIM) that help in the planning, designing, implementation and maintenance of the building. Present research work will use open source software and a standard file transfer data format to transfer information.

Eco-design incorporates environmental aspects into product development but often fails to provide quantitative and robust assessment, especially at the early development stages. Ex-ante life cycle assessment (LCA) is a future-oriented approach for assessing the environmental impacts of novel technologies (at low TRL), involving process scale-up and scenario analysis to model future industrial production.
This thesis aims to develop an eco-design approach for novel biopolymers, combining ex-ante LCA with multi-criteria decision analysis (MCDA). The specific goals are: i) combining robust and agile scale-up techniques to model the future industrial-scale production (TRL 9) of novel biopolymers (TRL 3-5); ii) integrating LCA and MCDA to compare and select the most sustainable alternatives while incorporating cost and technical aspects; iii) comparing the life-cycle performances of novel biopolymers (at low TRL) with commercial fossil-based polymers (TRL 9) considering scaling, uncertainty, and exploratory scenarios.
The eco-design approach will be developed for biopolymers still at the lab stage (TRL 3-5) for food packaging applications (e.g., nano-reinforced starch films). This involves employing and combining several techniques and methods. Life cycle industrial-scale models will be implemented by applying scale-up techniques (process calculations, simulation, etc.). Material properties will be considered using material substitution factors (MSF). MCDA will be applied at the output level (LCA scores, costs, technical aspects). Additionally, exploratory scenarios will consider potential technological changes and future energy mixes.
The expected contribution of the PhD thesis is a tool that integrates multi-criteria sustainability assessment into product development from early R&D. While biopolymers will be used as a novel product example, the eco-design approach may be adapted for other novel bio-based technologies.

This project explores one of the most prolific sources of natural products of our planet, the microbiomes of corals, to harvest novel chemical structures with potent bioactivities of promising use in biomedicine and biotechnology. The research addresses the current realization that marine host-associated bacteria produce many of the active compounds found in their host. It aims to uncover antibacterial and antifungal activities against notorious human- and aquaculture pathogens from a unique collection of symbiotic bacteria of octocorals from Portugal. Chemical extraction protocols will be optimized, and active metabolite extracts subjected to state-of-the-art LC-MS metabolomic profiling and analytical chemistry assessments. As such, this PhD proposal will develop minimally intrusive, sustainable methodology fostering bioactive secondary metabolites discovery from the seas to meet the global demand for novel therapeutics targeting multidrug resistant pathogens.

The lack of standard practices and platforms for assessing refurbishment strategies towards circular economy and its impact in global warming is a challenge for the decarbonisation of existing building stock. Incorporating data and feedback from designers and practitioners since early design stages is important to feed a multi-criteria dynamic process with multiple dimensions, which have to be assessed under a life-cycle perspective. To tackle this issue, a BIM-based “3R Assistant for Sustainable Refurbishment Design” will be developed, including three thematic areas: circularity assessment, material flows assessment and global warming impact. The digital platform will support existing building stock characterization and the design of corresponding refurbishment strategies in a BIM environment, which aim to facilitate standardized practices in construction sector, regarding circular economy in buildings refurbishments, with a positive impact in the mitigation of global warming and the decarbonisation of Portuguese building stock.

According to APREN, in 2019, wind energy was the main source of electricity in Portugal, with a weight of 27% when compared to other national electricity sources. However, current methods of mapping the wind resource and forecasting wind production still present considerable errors, resulting in the waste of significant amounts of wind energy produced. It is crucial to develop tools that minimize these errors, contributing to the development of new wind farms and greater use of the wind energy currently produced.
This project aims to develop a wind production forecasting system in Portugal that significantly reduces the errors obtained with current tools. This modelling system will be based on a mesoscale meteorological model optimized for Portugal considering its physical parameterization options according to the present atmospheric situation. As this research is a pioneer in Portugal, it is expected a strong scientific impact and political-socio-economic benefits for the country.

Perovskite solar cells (PSCs) are seen as the next generation of power source, contributing for fostering the use of photovoltaics in urban environments. Actually, their transparency, various possibilities of colors and high kWh/nominal power ratio offer to PSCs an opportunity to conquer markets that are not attainable by traditional silicon solar cells. However, efficiencies nearing and even outperforming other PV technologies remain insufficient to justify the move to industrial-scale. The main limitations reside in the yet to be proven long-term stability and the evidence that PSC can be produced at large-scale and low-cost. Thus, this workplan follows a research strategy that extends from fundamental research, namely by studying PSC operating phenomena for guiding future developments, to applied technology development, including focus on stability, upscaling and cost. The final goal is to demonstrate PSC innovative potential for street furniture by proposing a device-prototype.

This PhD project proposes an innovative Market Model suited to the concept of a renewable energy community, in which its members can interact through Energy Sharing and share, among themselves, the storage capacity of their stationary batteries or electric vehicles, Storage Sharing. In order to obtain an optimum energy planning for the community, the proposed multi-agent Market Model will be based on Artificial Intelligence (AI), with a hierarchical control structure, which will base their decisions on technical and economic aspects, considering the community load and renewable production forecasts and the restrictions, imposed by each member, for the community participation. To ensure the applicability of the proposed Market Model, a long-range wireless communication module will be developed, based on the Internet of Things (IoT), which will allow the monitoring and control, in real time, of the parameters of each member of the community.

Despite the importance of Land Use/Land Cover (LULC) maps to promote sustainability and good resource management, their production continues to be a challenging task. There are various factors that contribute towards the difficulty of generating accurate timely updated LULC maps, both via automatic or photo-interpreted LULC mapping. Data preprocessing, although being a crucial step for any Machine Learning task, is particularly important in the remote sensing domain due to the overwhelming amount of untreated data continuously gathered from multiple remote sensing missions. My PhD project focuses on the challenges found in automatic LULC classification tasks, specifically in data preprocessing tasks: imbalanced learning and anomaly detection problems are addressed as primary aims. Feature extraction and parameter tuning techniques are addressed as secondary/contingency aims.

Scientific Machine Learning (SML) is a recent trend involving the application of physically or mathematically-informed Machine Learning (ML) techniques to speedup classical numerical computations that are central to many fields of engineering and science. The application of SML techniques to Structural Design (SD) allows designers to explore and assess different solutions quickly, avoiding the need to use traditional Finite Element Models, which, despite being very precise, are computationally expensive and quickly become the bottleneck for experimentation.

My Ph.D. project will contribute to real-time 3D simulations in SD, which will facilitate the exploration of new designs and find optimal solutions without the need to have an expensive computational infrastructure. Furthermore, with this project, we will contribute to the connection of the Mechanical Engineering and ML community. These future cooperations will allow accelerating the digital transformation in manufacturing.

Development of acoustic transducers for underwater application. The main goal is to enable wireless communications underwater, with high data rates at close distances and lower data rates at longer distances. The transducers may also be used to monitor the soundscape of the underwater environment. The main challenge of this project is to evaluate and optimize the transducer’s efficiency in deep waters, at least 1 km.

This PhD project aims to develop a computational inverse Quantitative Structure Activity Relationship model, to optimize industrial catalytic epoxidation of small alcohols and alkenes. In turn, it will address sustainable manufacturing solutions towards this high economic value chemical reaction. The model aims to target the chemical space by implementing recent data science methodologies, which in turn feed and train machine learning models with data obtained from available experimental results. The project intends to embolden chemists and industries with a predictive model that eases reaction conditions, avoiding high cost quantum mechanics calculations and catalyst design by trial-and-error. It will also allow to find optimal epoxidation catalysts using biomass-based feedstock, thus avoiding the need of raw fossil-fuel commodities. This model will allow to reduce the environmental impact of these industries, lowering waste production, while generating higher product yields.

This project aims to prove to improve the reproducibility of scientific results in the area of ​​climate change through the construction of an interoperable search engine for sharing evidence of climate change, with a data model based on ontologies and able to link each evidence to the data and algorithms that support it. As testbed, a collaborative platform will be developed for the representation and retrieval of data sets and the remote execution of associated algorithms. It will use a Linked Data representation to allow the representation of evidences of climate change, georeferenced datasets and algorithms in a single knowledge graph.
The evaluation will be carried out with domain researchers, who will test the retrieval performance when compared to search engines based on metadata indexes and also the computing capabilities of the platform. Actual sensor data from the SAIL project will also be used, combined with other georeferenced data in the area of ​​climate change.

CO2 is the most concentrated greenhouse gas (GHG) in the atmosphere and, for this reason, it is the basis of climate policies, leaving other GHGs, such as CH4, in the background. CH4 is the second most prevalent GHG emitted by human activities; although its lifetime in the atmosphere is much shorter than CO2, CH4 is more efficient on trapping radiation than CO2, presenting a global warming potential 84-87 times greater over 20 years. Additionally, the methodologies currently employed for CH4 emissions mitigation, typically physicochemical-based procedures, comprise high-energy requirements and the production/release of CO2 to the atmosphere. Therefore, this project aims at evaluating the ability of microalgae and cyanobacteria (monocultures and cocultures) to fixate CH4 (and the resulting CO2), in order to convert it into biomass and high-value biocompounds for different biotechnological fields.

Global environmental awareness is leading the building industry to shift to a circular production model.
Regarding thermal insulation materials, although they contribute to reducing energy demand, the most used materials have high embodied energy. Bio-based thermal insulation products are an eco-efficient alternative to meet climatic goals since they stock CO2 and have lower EE.
The research work aims to develop a modern bio-based thermal insulation product based on reeds (Arundo Donax and Cortaderia Selloana), invasive species in Portugal. Its use has a positive impact in controlling the spread and creates a value chain for this resource. The objectives include: evaluating the thermophysical properties of both species; developing two prototypes of insulation panels; and analyzing the environmental and economic life cycle performance of the new insulation products. The research work expects to contribute by creating a bio-based material to be used by the building industry.

Life cycle assessment (LCA) is an approach that can support decision-makers in implementing efficient solutions for the construction industry and reducing carbon emissions. Recently, wood has gained popularity as a sustainable alternative material. However, most LCA studies of wood materials currently rely on old studies, not accounting for the dynamic of forests and the effects of climate change, like wildfires, on the carbon cycle. The Mediterranean region has always been prone to Wildfires, constituting a significant source of carbon emissions, and climate change exacerbates this problem by drastically increasing the frequency, intensity, and duration. Therefore, this study aims to analyze the effects of wildfires on the carbon cycle of forests for the current and future climate change scenarios and to assess the effects of it on the built environment. Finally, the goal of this project is to develop a dynamic LCA of wood-based products considering the effects of climate change on Southern Europe, using Portugal as a case study.

Geosynthetics have increasingly been used as a reinforcement material in a variety of geotechnical engineering applications (e.g., roads, railways, foundations, steep slopes, retaining walls). Reinforced soil relies on the transference of stresses from the soil to the reinforcement elements. Thus, in design, it is key to understand how mechanical damage of the geosynthetics influences the stress-strain-time response of geosynthetics and the soil-geosynthetic interaction. This project includes collecting high-quality experimental data, perform and analyse laboratory test results on the stress-strain response of geosynthetics and soil-geosynthetic composites before and after damage. Similar numerical models, using the finite element method will be carried out, implementing relevant constitutive equations. The numerical models will be validated using the experimental data collected. Parametric numerical tests will be performed on the influence of damage in geosynthetics. Finally, a general approach will be described to model common geosynthetic reinforcement structures, aiming to contribute to a more efficient use of both geosynthetic and natural resources using state-of-the-art modeling techniques.

As the nature of markets changes over time, so must the industry, in order to adapt and meet the increased consumer demands. Perfume design is a complex task that currently relies on expertise from perfumers, which affects its ability to adapt to paradigm changes. Thus, a novel smart Cyber-Physical System (CPS) is proposed as an innovative solution. The main goal of this project is to build a CPS intended for on-demand optimal design and production of perfumes. The CPS will be powered by Deep Neural Network (DNN) surrogate models. The components of the CPS together will constitute a disruptive systemwith self-managing and cognitive abilities. These components will be concisely interconnected through the Internet of Things (IoT) and use Cloud Computing as engine.

Growth dispersion is a problem that affects all stages of the aquaculture production cycle. It causes losses due to cannibalism at early stages, diseases in smaller and weaker fish, and finally a reduction in product quality and revenue at harvest due to weight dispersion. To minimize such problems, farmers periodically grade their fish stock to achieve a more uniform fish size. However, this increases production costs because it requires specialized equipment and human labor and causes stress to the fish and can also lead to mortality. The PhD project called Intelligent Fish Farming for Future (I4F project) aims to develop a software platform, a breakthrough and innovative machine learning based software to predict weight dispersion during the growth cycle based on rearing conditions (temperature, salinity, dissolved oxygen) to plan and determine optimized grading cycles that reduce costs, increase product quality and price, and reduce risks associated with the grading process. This is a breakthrough technology to improve aquaculture productivity, strengthen sustainability and environmental footprint. The I4F project will also provide an optimization model to reduce the number of classifications and reduce the use of resources (tanks) in the grading procedures, improving animal welfare and reducing costs.

The transportation sector accounts for the world’s largest share of the total growth in primary energy consumption. As a promising mobility tool, Electric Vehicles (EVs) are a viable alternative to decarbonize the future transportation system. Although the substantial challenges for power systems are likely to emerge by uncoordinated charging of the wide range of EVs, the coordinated charging (smart charging) strategies are essential to address the problem. The Charging Manager is a coordinator to manage the EV users and power system preferences and requirements; however, these preferences and requirements might be varied and contradicted for the EV users and power system. To tackle the problems, this study aims to exploit the flexibility potential of EVs by developing models and algorithms to support EVs smart charging having into considering the perspectives of EV users (minimizing the charging cost, minimizing the charging time, etc.,) and Charging Managers (maximizing the profit and quality of service).

An adequate description of travel demand in an urban area is crucial for mobility management, fostering an efficient urban metabolism. However, to this day, traditional data gathering was unable to effectively provide the needed decision support backed by statistically unbiased evidence for all kinds of trips. This thesis proposal takes a radically new perspective to demand characterization, relating the user’ profile to a predictable trip pattern, herein using all big data from Online Social Media (OSM), namely text, images, social links, and geodata. From here, the definition of homophily relationships (clusters) for associated users concerning mobility will allow, much more than usual, adequate characterization of transportation demand within a specific urban area. The ultimate goal is to provide the needed data-backed tools to allow governance to successfully fulfill users’ mobility needs, providing conditions to have a better sustainable environment for a city

According to European Commission (EC), 75% of the European Union (EU) building stock is energy inefficient according to the current standards. Furthermore, more than 85% of these buildings will still be in use in 2050. In Portugal, 66% of residential buildings were built before the introduction of the energy efficiency (EE) requirements for new buildings. In fact, 33% of the residential building stock was built before 2012 and shows low energy performance levels. In this context, the need to promote EE in buildings was brought to the European energy and climate policy agenda as a key factor in reducing energy consumption and emissions. In the same vein, Portugal has deployed several programs to promote EE. However, the evaluation of the EE measures to be financed is mainly based on cost-benefit analysis during the operation phase mainly accounting for energy and emissions savings, neglecting other potential benefits, and neglecting the potential impacts of other lifecycle phases, inherent to the investment in these types of actions. Nevertheless, some programs put new evaluation criteria into play such as job creation and compliance with national energy policies. With the foregoing in mind, this work aims to develop a new holistic approach based on the combination of mathematical programming models with Hybrid Input-Output – Life cycle Analysis (HIO-LCA) to help public Decision-Makers (DMs) in the choice and evaluation of EE measures that can be supported and adopted in the residential sector, which allows considering the potential economic, energy, environmental, and social impacts, and other benefits generated throughout its lifecycle, promoting a more adequate assessment of measures, potentially contributing to increase the their attractiveness. In this study the Portuguese residential sector will be used as case study, however, this approach is ready to be employed to any technology in any country as long as the available supporting data is adapted.

The problems of improving existing buildings’ energy performance, reducing energy consumption, and improving indoor comfort with its many consequences are well known. Considering increasing urbanisation and climate change, governments define strategies to enhance and measure buildings’ energy performance and energy efficiency. This work aims to improve buildings’ energy performance and energy efficiency by using machine learning techniques to analyse energy performance certification data. This study has the following two main objectives. First, to perform automatic classification of the energy performance certification of buildings analysing energy performance certification data and second to perform automatic proposals of energy-efficient retrofitting measures to improve the energy performance of buildings whose energy performance has been classified by achieving the first objective. The main contribution is to inform private and public building sectors on achieving enhanced energy performance and predicting energy-efficient retrofit measures towards improving their energy performance.

This PhD project aims at developing a framework for the integration of performance indicators on a holistic and sustainable management of the railway system. These indicators are going to be defined considering the railway system reliability, safety and availability as well as the cost analysis and environmental impact. The decision-making process is going to be supported by the use of digitalization of the system.

In the process of digital transformation in Industry – a sector that employs around 25% of active workers in Portugal – there is a need to evaluate workers’ well-being, as both musculoskeletal and mental disorders affect a great part of them due to lack of effective methodologies for guaranteeing their occupational health. In the proposed PhD project – integrated in the MIT-Portugal project Operator in a consortium of several acknowledged research groups and corporations – it is aimed to extract, study and understand the occupational factors that affect the well-being of the operator and design solutions to track job quality and prevent risk exposure. Henceforth, sensors will be used to monitor physiological and motion signals in work context and a software that from the signals assesses risk exposure will be built based on novel deep learning architectures. Finally, the operator will be provided feedback with relevant information, allowing a self-conscious change of conduct.

To promote a sustainable alternative solution to conventional materials (namely concrete and steel) supportive to the carbon neutrality strategy until 2050, this research proposes the development of a construction system for up to 5-storey buildings based on prefabricated wooden panels for successful application in the Portuguese context, either for new construction or rehabilitation.
Through this work plan, it will be possible to design, evaluate and optimise the system behaviour in structural, functional, and logistic terms, validating its performance in different scenarios; enhance and expand the prefabrication process and, consequently, its benefits; and finally, measure its sustainable performance through a life cycle assessment, mainly in terms of carbon emissions.
In addition to proposing a solution with a high potential for application and sustainable impact in Portuguese cities, it is expected to promote scientific enrichment in the theme of wood structural use in buildings.

The European Architecture, Engineering, and Construction (AEC) sector is currently undergoing a smart transformation, driven by the increased use of Building Information Modelling (BIM). This is particularly crucial today, as buildings need to present Net Zero Emissions (Net-ZEBs) during their whole life cycle, and BIM is an essential tool for the construction industry to improve the information flow. This project will develop a BIM-enabled LCA method that can facilitate low-carbon design under the transition for a smart AEC in Portugal.The findings of this project contribute to the achievement of UN Sustainable Development Goals related to sustainable cities and climate action targets, and the goal of reducing carbon emissions in the AEC sector.

The benefits of using connected and autonomous vehicles in transportation systems are dramatic, affecting mobility, the environment, safety, and the economy. These vehicles use many sensors and cameras to detect road signage and lane markings and to inspect objects around them. They also connect to other vehicles (V2V), infrastructure (V2I), or in short to everything (V2X) to share information between them, such as traffic and weather. Several studies indicate the need of using asset management processes to better manage the intrinsic aspects of connected vehicles, improving performance, resources, and budget planning, while lowering risks. It was identified how road design will evolve with a high adoption of automated vehicles, while some aspects will be improved, resulting in less resources spent on road construction. It was also examined how transport agencies can properly manage road assets, while it is expected that other elements should be managed as well, with the implementation of new infrastructure. Therefore, the main objective of this study is to define a methodology to manage road assets (or improve existing systems), considering all aspects of the future connected vehicles environment.

The conservation of coral reefs is under unparalleled pressure due to climate change. Coral-microbiome interactions play key roles in coral health in climate change scenarios, but our ability to benefit from such interactions to conserve the reefs’ ecological balance is unexplored. This project addresses this pressing demand using unprecedent microbiome manipulation of foundational octocoral species of the genus Eunicella. It aims to develop multi-species inoculants of octocoral-associated bacteria and test their effectiveness in boosting coral resilience towards temperature and pathogen pressures. This will be achieved via 1) phenotypic and genotypic screening of octocoral bacterial symbionts for host-beneficial properties; 2) development of multi species inoculants and in-vivo evaluation of successful host colonization in microcosm experiments; 3) in-vivo determination of the effect of microbiome therapy on host resilience using a high-end mesocosm facility. This project will increase our knowledge of coral-microbiome functioning, while delivering custom methodology useful for future coral reef restoration practices.

The research focuses on masonry arch bridges (MABs), which are vital elements of Europe’s transportation infrastructure. These historic structures have faced recent challenges, especially in dealing with natural disasters like floods, worsened by climate change. Additionally, increased traffic loads, aging bridges, and limited maintenance resources add to the complexity of ensuring their long-term functionality.

The project’s goal is to create a practical management approach to assess and enhance the resilience of MABs during and after flood events. By developing a risk-informed framework, we aim to optimize resource allocation and contribute to the preservation of our cultural heritage.

Marine spatial planning (MSP) has become the prime focus for many policy-makers, being
considered an important tool for promoting a better management of the oceans, through spatial and temporal planning of different maritime activities. Among the many threats faced in MSP development, climate change is considered to be one of the biggest. The intensification of climate-related impacts will continue to cause numerous problems to marine ecosystem services and depending human activities, thus affecting the development of a sustainable blue economy. Developing climate-smart marine spatial plans that properly integrate knowledge on climate risks and opportunities is fundamental. Hence, the main objective of the present proposal is to support such integration of knowledge by exploring the Portuguese case study to assess the vulnerability of national MSP initiatives and the blue economy to climate change.

Extreme weather and climate events occur in complex dynamic conditions. Understanding the role of large-scale patterns in these extreme events is paramount to increase our knowledge on their impacts and future evolution in the context of climate change. In this context, this PhD work aims at studying large quasi-stationary areas of high atmospheric pressure, or atmospheric blockings, which are known for their links to temperature extremes, as well as the induction of dry or wet areas in the mid-latitudes by affecting the tracks of cyclones. Methodologically, this PhD aims at introducing machine learning in the identification and classification of these blockings as well as in the association with extreme weather. Furthermore, this study aims at assessing the past and future changes in the evolution of these large-scale events through the use of state-of-the-art paleo and climate models, respectively.

Building Information Modelling (BIM) objects are at the heart of the transition of the Architecture, Engineering and Construction industry toward digitalization. BIM object creators are expected to comply with BIM object standards, taking into account compliance to Construction Product Regulations, standardization and interoperability requirements. This heavy task is limiting the widespread worldwide adoption of BIM processes. This work addresses this issue by (i) applying a systemized science-based approach for standardizing construction products data using the concept of Product Data Templates, (ii) creating a tool for the automation BIM objects data compliance to the latest standards and regulations, and (iii) setting the base for the creation of an open source BIM object repository.

Middle East countries lag in renovating their built environment to counteract the impact of climate change. The main reason for this is the low tax policy on fossil fuels, contributing to global warming and extreme weather events. This project aims to identify clean energy technologies and construction solutions that reduce the buildings’ dependence on fossil fuels. The project will analyze the energy, economic, and environmental impacts, which have not yet been assessed comprehensively in this region. As coastal cities are more susceptible to global warming impacts, Bandar Abbas, a coastal city in Iran, is considered a case study. Energy simulations are employed to investigate the performance of a set of reference buildings and determine the most effective measures to cope with this impact under different climate scenarios. A tool will be developed to assist in the decision-making process of planners and architects. The expected outcomes will ultimately influence policy-making in those coastal cities.

Nowadays several heritage buildings have been suffering operational changes over the last decades. In fact, most cultural places are victims of their own success and suffer from being intensively explored as touristic sights. The management process plays a crucial role in their preservation and it is becoming more complex due to current touristic pressures and heritage challenges. Therefore, it is urgent to study ways to characterize the indoor environmental conditions, identify the possible impact of tourism and define strategies that help in the preservation of cultural heritage. In this context, the doctoral research explores monitoring and simulation procedures to identify causes of inappropriate indoor environment conditions, and the respective risks of degradation, and discuss the most appropriate strategies to preserve heritage patrimony. Considering the indoor environment as a set of hygrothermal, lighting conditions and the particulate matter, a review of the evolution of guidelines for collections was made aiming to assess the actual requirements for conservation and identify gaps that deserve improvements and further research. The research englobes continuous and systematic monitoring campaign followed by a correct assessment of the indoor conditions according to literature approaches. Joanina Library is the case study of this project. For a comprehensive assessment of the indoor environment, a simulation activity is also being carried out. The case study is being modeled using appropriate software, EnergyPlus, in regard to the hygrothermal behavior, and Radiance, for the lighting environment. The data from the monitoring campaign is used for the validation of the models. For this purpose, the research methodology will include the study of the indoor environment by recurring to monitoring and simulation while assessing the efficacy of passive and management strategies for heritage buildings.In this way, the adopted methodology will answer the main research question regarding the need or not to use active systems to control de indoor environment in heritage buildings in today’s context. Not only today, but eventual climate changes and increasing touristic pressure will affect the preservation conditions. Important scientific and technical contributions are expected. The project results will allow us to understand how to use the monitoring campaigns and simulation to characterize the indoor environment and assess the quality of control strategies in this type of space.

Smart paints incorporating thermochromic and self-cleaning properties can serve as a color-based and reversible sensor, effectively alerting drivers to the presence of icy road surfaces. This innovative technology signals a decrease in pavement friction, prompting drivers to adopt safer driving practices and thus reducing the frequency and severity of road accidents. Thermochromic materials (TM) exhibit color changes at a specific transition temperature (TT), a characteristic stemming from their chromogenic core. In the context of asphalt paving, this pioneering application involves the use of TM with a TT closely mirroring the melting point of water (0 °C). This research represents a multidisciplinary endeavor, characterized by a substantial laboratory component dedicated to the development of intelligent paints and their practical application in road infrastructure. The study also encompasses comprehensive evaluations, including a realistic driving simulator setting. This approach aims to assess the viability, technology transfer, and the impact of the final product on road safety.

Coastal aquifers present in Mediterranean climate areas are susceptible to the Climate Change’s effects, which can lead to their degradation, and thus, to severe environmental, economic, and social consequences. Campina de Faro aquifer (Faro, Algarve, South of Portugal) is already considered to have poor quality status, therefore, to reduce the uncertainties of the models used for water assessment and management it is suggested to integrate the aquifer’s actual knowledge (data from authorities and national databases), with new data coming from a monitoring programme. Using a set of methods like hydro-chemical techniques and groundwater modeling will be then possible to better understand and forecast the aquifer’s state/pressures/driving forces and discuss the best combination of management solutions, which can provide the lowest chemical alteration/risk of contamination, meanwhile guaranteeing the preservation of water levels. The expected results will not only allow the better understanding of the study area but will be also of interest to water-stressed regions with similar conditions.

This research is related with the ongoing Greenshoes 4.0 mobilizer project in which the required computational modelling tools will be developed. The suggested approach in this project will improve the modelling capability, by combining the available codes with AI methods, thus, noticeably minimizing the dependence on the persons involved in the design processes. This approach is mandatory due to the inability to process systematically, efficiently and in a timely manner, the large amount of available information.

Additive manufacturing (AM) technology is a key driver for the digital transformation, as it promises to dramatically change business and innovation models, by shrinking supply chains and altering the global economy. However, findings reveal a lack of research addressing the most prominent trends currently driving AM: such as decentralized production of customized products and small-to-medium volumes transition, thus highlighting the disruptive nature of the expected changes in the industry. Aligned with the MPP2030 funded seed project – Introducing 3D Printing into the Production Chain – the goal of this Ph.D. is to use data-driven techniques and optimization approaches to provide decision-makers with information on how AM techniques will impact processes. The successful accomplishment of this goal will have a positive impact on the automotive, furniture, and semiconductor sectors, by achieving a framework to provide practical guidance on the steps needed to optimize AM processes.

Environmental pollution is one of the biggest challenges at the global level. For years, the environment and the natural resources of the planet have been neglected by governments worldwide, mainly due to their agenda in developing countries. The consumption of natural resources is increasing every day, leading, for example, to air and water pollution reaching levels never seen before, through the release of pollutants into these environments. Developing countries due to poverty, lack of investment and rigorous damage to the environment are more affected by environmental pollution.
Preventing and reducing environmental risks is still a challenge today. Several international organizations publish reports every year, showing a higher number of deaths caused by environmental pollution. By reducing environmental hazards, we are helping our planet become more habitable, reducing the number of deaths caused by this scourge.
With the increase in the number of computing devices in our society, the amount of data generated and available has increased exponentially. The information contained in the data is a precious element to extract knowledge. In this sense, my objective is to apply a set of Classic Machine Learning and Deep Learning models in the context of anomaly detection to improve the sustainability of Wastewater Treatment Plants (WWTPs). The conception of these models will alert the decision-makers so that they can act more quickly and accurately. Along with the models’ conception, Explainable AI will make it possible to understand whether, in detecting an anomaly in these companies, which features impacted that classification. Through these models, the objective will be to optimize the sustainability of WWTPs in their various fields. As these infrastructures are part of our society, optimizing their sustainability will be reflected in the conception and development of more sustainable cities.

Machine learning (ML) is often criticized for its lack of interpretability and transparency. In order to tackle these problems, the field of explainable artificial intelligence (XAI) emerged in recent years. While XAI offers strategies to gain insight into how models learn and their decision-making processes it lacks the concept of acceptance of AI. Interpretability and transparency play a role in acceptance but do not fully encapsulate all factors that contribute to it. Thus, research into what constitutes acceptance needs to be made.
As part of the MIT-Portugal project OPERATOR, the proposed PhD project will contribute to XAI and acceptance of AI systems within the industrial workforce to produce recommendations based on cognitive and biosignals monitoring and by developing quantifiable measures of acceptance and trust for AI systems, depict understandable and interactive visualization schemes while also designing ML models that incorporate these measures.

Micro-reinforcement using engineered fibres or particulate inclusions such as tyre shreds has huge potential for improving the performance of coarse granular materials used, for example, as a road base or railway ballast. My PhD project aims to develop understanding of the micromechanics of micro-reinforced coarse granular materials, through fundamental experimental studies centred on element testing of scaled and full-size materials in loading paths representative of both service conditions and maintenance operations. The form of the micro-reinforcement will be optimised to increase its effectiveness. The resulting understanding of how different micro-reinforcements stretch and deform, restrain the movement of grains and influence packing will be used to develop analytical models for the mechanical behaviour of the reinforced composite, which will be used to further optimise the form of the micro-reinforcing elements (at industrial scale) and inform future design guidance.

Peatlands are unique ecosystems providing key contributions to carbon storage and climate regulation. However, human impacts are increasingly degrading these ecosystems, converting them from sinks to sources of atmospheric carbon. To mitigate and prevent further degradation and help accomplish the Paris Agreement, it is needed to assess the current status of these ecosystems and anticipate the impacts caused by the distinct drivers of degradation. Over the last decades, multiple factors have accelerated peatland degradation in the region, including changes in climate, colonization by invasive plant species and human-driven changes in land-use and land-cover. This project will focus on the peatlands and organic soils of the Iberian Peninsula and The effect of these processes over them and the services they provide remain poorly known.

Climate change effects and impacts have exponentially increased causing global concerns across all sectors. In response, policymakers began to plan policies and responses to mitigate and adapt to these issues. However, due to climate change unique complex problems (e.g. short time to act, global impacts, high unceranty, high costs) it has hinder and stifles decision-maker’s ability (or taken actions) to adress the many problem.

Often, policy and decision-makers are not been able to meet the necessary climate criteria needed to progressively act as they are inexperience with a problem of such magnitude, using outdate policy approaches, and lacking policy infrastructure to monitore, evalute and monitore (MRE) the progress of the decision they made. The latter been important, as climate change may influence and alters the enviroment were these policies are implemented, potentially rendering them useless or worst, creating more damages.

This project seek to explore the constrains of policy approach to climate change, the challenges that policy and decision-makers, from key sectors, face, and how come the introduction of innovative policy instruments and tools (climate indicators and real-time data gathering) for MRE decisions may improve the rate of positive outcome throught the prediction of policy tendencies.

Earth is mostly composed of oceans, which are the least explored resources on our planet. The European Council predicts that in 2050, 50% of electricity needs will come from marine renewable energy systems. In Portugal, the production of marine renewable wind energy started in 2011, with the installation of a 2MW floating wind turbine platform. Recently, in 2020, the first offshore renewable wind farm, Windfloat Atlantic, with a capacity of 25MW, was inaugurated. In this sense, this Ph.D. research project intends to develop an integrated fatigue methodology applied to support structures for wind towers in an offshore environment, more specifically the fatigue damage assessment based on multi-scale modelling and in full-range fatigue models. The benefits to the environment are obvious, since the proposed methodology will allow the optimization of design fatigue criteria, contributing to a more responsible renewable energy structural systems generation.

Pacifiers are excellent to calm babies, but there are numerous risks about its prolonged use. The current approaches used to design pacifiers are strongly based on accumulated experience. In a previous work of the candidate, it was shown that realistic computational models allow obtaining concrete data on the effects of pacifiers, opening the route to the digital transformation of the pacifier design and manufacturing methods. The work proposed in this project aims at developing a novel pacifier design methodology together with an innovate adaptive pacifier, both conceived with the support of computational modelling tools. The adaptive pacifier will comprise an intelligent sensing system that will allow monitoring its operation in real-time. Data mining will be applied to identify useful patterns in the collected data, which will be used to parameterize the pacifier usage limits, taking into consideration each user suction pattern, thus allowing continuous pacifier usage supervision.

The motivation for the present research plan is to assess the feasibility of a deepwater expansion solution at the port of Leixões, resulting in the first deepwater port terminal in the Portuguese Northwest coast. Regarding the reorganization recently verified in the waterborne freight transport, there is the corresponding need for adapting sea ports existing infrastructures to later receive larger-sized cargo vessels. This proposal thereby addresses Portugal’s competitiveness through its national port system and intends to establish a key tool in defining intervention priorities, pondering the costs, benefits and impacts of such construction work. The port of Leixões was chosen for the case study as it represents a relevant trade share in the Northwest coast of Portugal and Spain and has potential to broaden its hinterland through the increased capacity of handling containerized cargo.

Current plantwide statistical process monitoring (SPM) methods face problems in detecting and diagnosing localized faults in high-dimensional complex processes, compromising personal safety, quality, efficiency and the environment. They are also unable to handle the multiplicity of time scales that are relevant for fault detection and to conduct degradation prognosis activities. This project proposes a novel framework that brings causality concepts to plantwide distributed SPM, improving fault diagnosis, and defines the optimal aggregation level of variables, for improving monitoring performance. Furthermore, a multiscale decomposition of the time series will be conducted to separate the time scales for monitoring (high frequency bands) from those that represent slow degradation modes (low frequency bands). The proposed methodology is called MAG-NET (Multilevel and multiscale AGgregation of causal NETworks) and it unifies plantwide detection, diagnosis and prognosis.

The future of cities will depend on their formal and functional relationship with nature. Thus, operative concepts are needed for addressing specific realities and challenges, informing integrated strategies for sustainability. This proposal starts from an evolutionary analysis of the concept of “urban-rural space”. Three case-studies will serve as reference: Lisbon, Barcelona and Bucharest; studying in each one the historical urban fabric, the planned expansion and the dominant architectural types, as well as their relationship with green spaces (public/private, leisure, agricultural production or environmental preservation). The study of the historical development and genesis of each territory will enable an inventory of solutions suited to their characteristics, which, given their diversity, allow us to think about other cities. This study will result in a monograph, a handbook and an exhibition, aimed at academic and general audiences.

The continuously increasing use of glass in buildings as well as the higher frequency of accidental or man-induced blast events has motivated significant research effort in recent years. The prosed research is established in this framework and aims to contribute to the characterization of the behavior of glass façade panels subjected to blast action. The work plan addresses aspects that haven´t been tackled yet and will pave the way for the establishment of design guidance regarding blast action in glass structures, aligned with Eurocodes design philosophy. The work entails an extensive parametric analysis, based on Finite Element Models (Abaqus software) calibrated with experimental data, covering the most representative structural parameters, leading to a large enough data for statistical validation regarding the typologies and material commonly used in practice and a vast extent of blast actions.

Wire-Arc Additive Manufacturing (WAAM) is an emerging additive manufacturing (AM) process that uses wire as the feed-stock and an electric arc to melt it. WAAM has a promising sustainability potential due to its better material efficiency, higher build rates, and low cost. However, research on its sustainability from a Triple-Bottom-Line perspective i.e., environmental, economic, and social impacts is still new. This PhD plan proposes to evaluate the WAAM´s environmental, economic, and social sustainability using Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (S-LCA) methodologies, respectively. Based on these assessments, process parameters, variables, and production scenarios ensuring sustainable production by WAAM will be recommended. Furthermore, an online platform will be created to quickly calculate and compare the environmental, economic, and social impacts of WAAM and other manufacturing processes to select the most sustainable approach.

The project main goal is to enhance the understanding and information retrieval of the seasonal formation of melt ponds on the Arctic Sea ice. Melt ponds have a great importance in the context of climate change and Arctic energy budget. They lower sea ice albedo (fraction of incident radiation that is reflected by the surface) contributing to further ice melt. However, information on these features is currently very scarce. To tackle this issue, this research aims at using a combination of differente types of satellite sensors (namely Synthetic Aperture Radar and optical) and Artificial Intelligence (AI) methods, to help generate new, timely and more accurate information on these Arctic sea ice features.

Water efficiency has an increased relevance in the current climate change context, establishing great potential in the adoption of water management best practices, namely to face the increase in water scarcity and water stress. By promoting the efficient use of water in buildings and recognizing this practice as a critical factor for environmental sustainability and for the achievement of goals established at international and national level, the main objective of this research is to assess the impacts of reduction consumption in buildings in urban water supply and drainage systems. The optimization of water use translates into a reduction in the volume of water abstracted and wastewater, with consequences that may affect the hydraulic and sanitary performances of systems. Therefore, to plan the investments, ensure the correct functioning of the public networks and environmental protection it is important to study the impact on existing infrastructures and possible mitigation measures.

This proposal aims at the development of an industrial Human-Robot Collaboration (HRC) framework, within the scope of industry 5.0 and the digital transformation in manufacturing. It intends to address the growing number of Work-Related Musculoskeletal Disorders (WRMSDs),bringing innovation in ergonomic interventions by providing valuable collaborative robot assistance according to the worker needs through the development of human-aware and human-care control strategies, powered by Artificial Intelligence and interoperable wearable technology. Novelty includes bi-directional interaction between human and robot by identifying and monitoring in real-time the worker’s motion and fatigue during a collaborative task and tailoring the robot physical-collaboration accordingly. Expected benefits include the operator’s well-being, healthier and safer working environments, increased productivity and reduction on the costs derived from WRMSDs, while fostering companies’ industrial competitiveness.

Construction faces increasing sustainability performance, material and manpower scarcity and a growing shift into urban renovation processes, demanding time, cost and material efficiency. High-performing renovations can significantly reduce the building life cycle impact, but as Architects and Engineers transition to the BIM methodology, they find that computational tools and data to support sustainability and circular economy goals are primarily non-existing. This research aims to support informed decision-making on building renovation scenarios at early design phases from a circular material management perspective. The specific goals are: i) collect and define design criteria that guide construction designers to informed decisions, ii) develop BIM-based tools that identify the optimal design scenarios, and then iii) optimise the proposed tools with machine learning techniques. The expected results are a bim-based methodology that, through computational techniques, becomes more accurate over time and a database with the characterisation and environmental assessment of several renovation projects.

Transportation systems have a significant impact on the sustainability of urban areas. The economic impact on budgets and travel time and distance due to climate change is evidence of this. The present thesis aims at defining a holistic framework for the risk-based management of transport infrastructure networks. To anticipate the effects of climate change and ensure, in the medium and long term, that network service levels and resilience remain at acceptable levels, it is necessary to consider the level of asset exposure to disruptive events. In addition, transportation systems are composed of different types of assets, which react differently to different stimuli, depending on the surrounding environment.
The proposed work intends to establish an alternative to conventional infrastructure risk assessment procedures by accommodating, in a single methodology, the performance of different asset types, subjected to different hazards, independently of their particular characteristics.

Historic masonry structures (HMS) are often constituted by the assemblage of blocks with variable dimensions engendering complex arrangements. Furthermore, cross-sections’ walls are sometimes made by more leaves, connected through the thickness in a more or less efficient way such that the real out-of-plane (OOP) behaviour is difficult to predict. Such geometrical uncertainties suggest investigations based on probabilistic approaches where masonry patterns are characterised by certain statistical variations. This PhD thesis aims to investigate the influence of masonry arrangements on the structural behaviour of HMS. At first, a masonry pattern generator will be developed to create random patterns that will be categorised and investigated. Then, advanced numerical models based on the discrete element method (DEM) will be adopted to perform parametric and sensitivity analyses aiming to develop easy-to-use analytical tools and provide useful guidelines for researchers and practitioners.

The release of colossal amounts of carbon dioxide (CO2) into the atmosphere is causing catastrophic climate changes and conditioning the future of the Earth. To preserve our planet, it is imperative to control and minimize the CO2 release. However, the reduction of CO2 emissions remains a challenge as the global energy consumption is increasing incessantly and is still mainly supported by fossil fuels. Currently, the capture of CO2 to store in underground reservoirs is well-known, but it can be considered a waist an abundant feedstock for valuable chemical products.
Concerning these problems, the project aims to develop advanced porous metal-organic framework-based platforms to optimize the capture of CO2 and, simultaneously, convert CO2 into an added-value product in various industries. The combination of both functionalities is a strategy for an efficient and cost-effective reduction of the CO2 levels, and allow the production of useful products from an inexpensive feedstock.

Worldwide ecosystems are currently experiencing increasing fire weather conditions caused by environmental changes and human activities, which have been amplifying the severity and length of fire seasons. Therefore, ecosystems’ ability to recover from fire may be impaired. Moreover, more frequent and severe hot and dry extreme events, projected to increase in many fire-prone regions, may result in faster fire recurrence than recovery times, leading to loss of ecosystem resilience to extreme events and, potentially, forest degradation. The current PhD proposal aims to analyse the post-fire vegetation recovery globally, focusing on fire-prone regions but also on areas which are observing an increment in fire weather conditions and fire occurrence. By combining long-term remote-sensing datasets with dynamical vegetation and land surface models’ outputs, this project aims to identify patterns and quantify trends in post-fire recovery data to assess changes in fire disturbance regimes, both under present and future climate conditions.

“Mod City” intertwines concepts of “smart”, “ludic” and “hackable” cities. Proposes a new conceptual framework for urban challenges, in which “mod”ification becomes part of the process of community co-creation with a view to integration and socioenvironmental sustainability. The term “mod” appears in the culture of computing, digital and games,
and advocates a desire for collaboration and sharing; conveyed in this project via gamification, enhancing social equity, positive interdependence, agency and confrontation of ideas.
Transdisciplinary to the fields of Art, Science and Technology, this art-based actionresearch
project focuses on the collective identification of urban sustainability problems and their subsequent resolution, with the co-creation of playful instruments that converge digital and analog. This experimental research in portugal, contemplates an in-depth critical reflection on the use of civic media in the co-created process of a sustainable urbanism.

Digital twin (DT) is one of the most modern and promising technologies in realizing smart manufacturing and implementing Industry 4.0. DT offers opportunity to integrate the physical world with digital world with a seamless data source. Civil engineering industry, in general, is facing many challenges in the process of digital transformation to improve efficiency and technology to meet the current growth rate of the economy. DT technology has the potential to transform and improve the exploitation and management of infrastructure in civil engineering, especially in the service phase. Based on DT model, managers and maintenance operators can test different scenarios, improve efficiency, and make accurate decisions in maintenance of the structure, leading to reduction of management and other regular monitoring costs, as well as accurate prediction of risks during the lifespan of the infrastructure. This research focuses on building a digital twinning model for the continuous structural health monitoring of critical infrastructure in the service phase.

The main research objective is developing sound and realistic aggregation models based on optimization algorithms for the operation of an aggregator maximizing the utilization of demand-side flexibility while minimizing the prosumer’s energy cost and discomfort. The methodological approach consists of developing: mathematical models encompassing the physical behavior of demand-side resources (loads, generation, and storage); algorithms combining mathematical programming and evolutionary computation to deal with hard combinatorial models to optimize prosumers’ responsiveness to incentives set by the aggregator; adaptive optimization processes in face of changes occurring in real-time. The main challenge is how to characterize the prosumers’ responsiveness and the main novelty of the research is to inset learning capability in the aggregation optimization algorithms. The results can be used by any entity performing the role of aggregation of demand flexibility contributing to optimal power system management.

This project aims to design and implement a cognitive two-layer Cyber-Physical System (CPS) process to separate methane from CO2. The CPS-PSA will be powered with Artificial Intelligence (AI) and the automated smart system with Internet of Things (IoT) that will yield autonomy and self-management capabilities which, in turn, will lead to high-efficiency and sustainable operation. Altogether, the project is aligned with Portuguese plans for science development and the United Nations Sustainable Development Goal 9.

Ni alloys, particularly INCONEL alloys, present very low machinability, making them a popular subject for machining studies. Generally, using high amounts of cutting fluid is needed to machine INCONEL alloys, however, the use of these coolants is extremely hazardous for the environment and worker safety, while increasing the overall process cost. To minimize these issues, many solutions can be adopted, such as the use of coated tools, lubrication techniques or even by trying to optimize the process itself. In this work, different sustainable INCONEL machining techniques will be tested, evaluating tool-wear, process vibrations and cutting forces, as well as the machined surface integrety of the workpiece material. Data obtained from these tests will be used to develop and validate process optimization models, with a focus on improving the process’s sustainability while retaining good production quality, as well as predicting tool-wear and produced surface quality.

Understanding climate change and its impacts is one of the scientific challenges of the 21st century. The increase of the Earth’s atmosphere and oceans mean temperature since the Second Industrial Revolution is affecting our world in many ways. Extreme events, such as floods, droughts, hurricanes, and storms are more frequent and are becoming more intense.
One of the most selected approaches to understanding the impacts of climate change on beaches and estuaries is through simulation based on numerical models, that are developed using mathematical descriptions of the main physical processes that are responsible for the dynamics of those areas. However, models with high resolution, which results are more reliable, need considerable computational times and the analysis of complex scenarios could be limited by the available computational resources and the time needed for their simulation. Therefore, the main objective of this PhD project is to develop a new methodology that will apply data science concepts to implement and configure emulators capable of surrogate high-resolution numerical models, aiming to evaluate the impacts of climate change, extreme events and anthropic intervention in estuarine and coastal zones. These emulators will reduce the computational resources needed for long-term simulations, making it feasible to apply them in operational forecast platforms, which will optimize the decision-making process regarding the adoption of mitigating measures against the impacts of climate change.