2023 Call for Seed Grant Proposals<br

Scientific Area: Climate Science & Climate Change

Abstract: This research proposal aims to address uncertainties surrounding the carbon sequestration potential of abandoned croplands, a strategic focus for climate change mitigation that avoids conflicts with food security and offers co-benefits such as enhanced biodiversity and climate preparedness. We propose to map cropland abandonment globally and quantify aboveground biomass and soil carbon accumulation under different restoration scenarios (active vs passive restoration). Using a combination of remote sensing, GIS mapping, and meta-analysis techniques, we will identify regions with the highest carbon sequestration potential and assess the impacts of various factors on carbon accumulation rates. The outcomes of this research will contribute to the development of evidence-based policies and strategies for climate change mitigation and land management, highlighting the most effective restoration approaches to maximize carbon sequestration, biodiversity enhancement, and climate preparedness.

MIT PIs:
Cesar Terrer, Assistant Professor, Department of Civil and Environmental Engineering

PT PIs:
Cristina Cruz, Assistant Professor, Universidade de Lisboa & cE3c -Center for Ecology, Evolution, and Environmental Change.

This grant is renewed until August 31, 2025

Scientific Area: Climate Science & Climate Change

Abstract: Wildfire smoke causes substantial negative environmental and public health consequences and bears high social and economic costs. This research project is aimed to fill a major gap in the literature by investigating the effect of wildfire smoke on migration. We, thus, analyze how humans adapt to the increased frequency and severity of wildfires due to climate change by choosing to change their location of residence. Using annual county-to-county migration flow data from the United States Internal Revenue Service combined with satellite data on wildfire smoke, we estimate how persistently high particulate matter concentrations cause people to move within and out of California. Understanding how wildfire smoke impacts migratory patterns entails important lessons concerning labor and housing markets, fiscal policy, and environmental justice.

MIT PIs:
Christopher Knittel, Professor, Sloan School of Management

MIT Co-PIs:
Benjamin Krebs, Postdoctoral Associate, MIT Center for Energy and Environmental Policy Research

PT PIs:
Maria A. Cunha e Sá, Full Professor, NOVA School of Business and Economics (NOVA SBE), Lisbon
Renato Rosa, Assistant Professor, Centre for Business and Economics Research (CeBER), University of Coimbra
Joana Silva, Associate Professor, Católica Lisbon School of Business & Economics

Scientific Area: Climate Science & Climate Change

Abstract: Wildfires that degrade air quality are increasing in intensity worldwide, and in Portugal in particular. However, the human exposure implications of the specific toxic pollutants that can be produced or mobilized by wildfire smoke is uncertain. The objective of this work is to estimate the relative impact of large wildfire emissions to overall exposure-related metrics for two toxic substances emitted by wildfires: polycyclic aromatic hydrocarbons (PAHs) and mercury (Hg). To do so, we will use global-toregional atmospheric modeling and data analysis to simulate the emission and dispersion of these two pollutants from large wildfires, focusing on southwestern Europe. We will use improved emission inventories and model-measurement comparisons to improve the ability of models to accurately represent these substances and their impact from wildfires. We will then calculate wildfire contributions to exposure-relevant metrics, and explore modeling of future climate impacts.

MIT PIs:
Noelle Selin, Professor, Institute for Data, Systems, and Society (IDSS); Department of Earth, Atmospheric & Planetary Sciences

PT PIs:
Alexandra Monteiro, Principal Researcher, University of Aveiro

This grant is renewed until August 31, 2026

Scientific Area: Climate Science & Climate Change

Abstract: The extent and dynamics of the semi-permanent North Atlantic high-pressure system known as the “Azores High” influence the weather and climatic patterns of the Azores Islands, affecting vast areas of western Europe, northern Africa, and the Mediterranean Basin. Climate change is increasing the intensity and frequency of extreme weather events in the Azores, and globally. We propose to use atmospheric water vapor data from GNSS and reanalysis products from an atmospheric general circulation model to investigate the dynamics of weather fronts in the Azores, and to explore how extreme weather events are affected by climate change. The proposed study will use new GNSS approaches to probe small-scale atmospheric structures with high-temporal resolution, which will be cross-compared with a MERRA2-based atmospheric reanalysis approach developed at MIT. Atmospheric studies are important for understanding climatic changes in extreme weather, and for mitigating weather-related hazards such as torrential downpours and landslides for the Azoreans.

MIT PIs:
Dhiman Mondal, Research Scientist, MIT Haystack Observatory

MIT Co-PIs:
Pedro Elosegui, Research Scientist, MIT Haystack Observatory;
Lucy Brock, Undergraduate Student, Department of Earth, Atmospheric and Planetary Sciences

PT PIs:
Pedro Mateus, Professor, Faculty of Science, University of Lisbon, Portugal
Virgilio Mendes, Professor, Faculty of Science, University of Lisbon, Portugal

Additional Collaboration: Scott Paine, Research Scientist, Center for Astrophysics | Harvard & Smithsonian, USA

Scientific Area: Climate Science & Climate Change

Abstract: This collaborative project develops a new predictive and prescriptive analytics framework for building climateresilient multi-energy systems. Our work integrates the planning of distributed energy resources (DERs) and demand response (DR) programs for improving the resiliency of renewables-dominant power systems in the face of extreme events. Firstly, we develop a two-stage stochastic optimization approach to allocate DERs and grid hardening resources in the pre-disaster phase, and trigger DR and restoration operations in the post-disaster phase. Secondly, we consider resiliency and low-carbon operations as design requirements for DR programs and comparatively evaluate aggregator-coordinated and automatic response mechanisms for various disruption scenarios. Thirdly, we refine our allocation and response strategies to include endogenous uncertainty representation which captures the impact of planning decisions on the key parameters governing energy demand and renewable supply. Our research will contribute to a decision-support system and practical strategies for climate-resilient energy systems in Portugal and New England regions.

MIT PIs:
Saurabh Amin, Associate Professor,  Department of Civil and Environmental Engineering

PT PIs:
Filipe Joel Soares, Centre for Power and Energy Systems (CPES) of INESC-TEC (University of Porto), Senior Researcher, Doctor.

This grant is renewed until August 31, 2025

Scientific Area: Sustainable Cities 

Abstract: A key component for tackling climate change is ensuring that buildings are energy efficient and sustainable. A significant portion of building energy consumption is wasted due to improper operation and control. For instance, in commercial buildings and university campuses, energy is often wasted in heating or cooling unoccupied spaces. Furthermore, even an initially “optimal” system will not operate according to design specifications indefinitely as equipment degrades and building usage evolves over time. This project will develop a new approach for “continuous commissioning” of building facilities to ensure that buildings operate at peak efficiency with a minimum expense in sensors and computation. This project will develop a real-time building monitoring and control framework that provides actionable information to operators. The solution aims to be low-cost and scalable, while helping operators better maintain existing equipment and make wellinformed decisions for building equipment and control retrofits.

MIT PIs:
Steven Leeb, Professor, Department of Electrical Engineering & Computer Science

MIT Co-PIs:
Les K. Norford, Professor, Department of Architecture
Daisy H. Green, Post-doctoral Associate, Department of Architecture

PT PIs:
Ricardo Gomes, IST RC, Instituto Superior Tecnico, Lisboa

This grant is renewed until August 31, 2026

Scientific Area: Sustainable Cities 

Abstract: With the fast development of information and communication technologies, demand-responsive transit
(DRT) is considered salvation for failed and expensive traditional fixed-route public transport services,
especially in low-density areas. However, evidence shows that in many cases, these services failed to
stand up to the promise and saw very low ridership, which is not substantially higher than the system they replaced without any reduction in operational costs. However, future technological development of automated vehicles should bring a massive cost reduction that might change the course of DRT.

In this project, we analyze both the operational side of automated DRT through optimization methodologies and the economic regulation and incentives through game theory and bi-level programming to optimize accessibility for transit-deprived areas. An agent-based simulation is used to validate the results. We intend to introduce a systematic approach for evaluating real-time incentives such as tolling and subsidies to achieve desirable user and system-level performance.

MIT PIs:
Moshe Ben-Akiva, Professor, Department of Civil and Environmental Engineering

MIT Co-PIs:
Amir Brudner, Dr. Postdoctoral researcher at the Intelligent Transportation Systems Lab

PT PIs:
António P. Antunes, Professor, CITTA, Department of Civil Engineering, University of Coimbra, Portugal
Gonçalo Santos, Dr. Postdoctoral researcher at CITTA, Dept. of Civil Engineering, University of Coimbra, Portugal
Anne Patricio, Ph.D. student at CITTA, Dept. of Civil Engineering, University of Coimbra, Portugal

Scientific Area: Sustainable Cities 

Abstract: The goal of this project is to develop a computational framework to advance the fundamental science of safely routing and planning the motions of a large fleet of Unmanned Autonomous Vehicles (UAVs) in complex urban environments. The approach consists of three major components: 1) high-fidelity computational fluid dynamics (CFD) to obtain realistic wind conditions in urban scenarios, 2) a novel routing algorithm that allows the UAVs to temporarily ride public ground transit vehicles to conserve energy, and 3) a rigorous mathematical motion planning framework to navigate UAVs safely and effectively in realistic urban windy environments given a set of user constraints. The outcome of the work will facilitate the adoption of delivery of goods by UAVs, which has the potential to revolutionize logistics by shortening last-mile delivery times and improving reliability. The technical approaches explore the use of machine learning combined with rigorous mathematics to provide provable correctness guarantees.

MIT PIs:
Chuchu Fan, Assistant Professor,Department of Aeronautics and Astronautics

MIT Co-PIs:
Adrián Lozano-Durán, Draper Assistant Professor of Aeronautics and Astronautics

PT PIs:
António Pedro Aguiar, Full Professor at the Dept. of Electrical and Computer Engineering; Head of Research Center for Systems and Technologies (SYSTEC); Faculty of Engineering, University of Porto

Scientific Area: Earth Systems: Oceans to Near Space

Abstract: This project aims to develop an open-source physics-based model of the ionosphere-thermosphere
system in order to enable real-time forecasting of neutral and charged particle densities, which is critical for predicting atmospheric drag and modeling radio signal propagation for low-Earth orbit satellite operations. The proposed model builds upon MIT’s physics-based model of the neutral thermosphere by incorporating charged particles and complex chemical reactions of various species.
The Instituto de Astrofísica e Ciências do Espaço will collaborate on this project to provide valuable validation data and complementary expertise in space physics. The approach will be validated using observational data as well as experimental data from satellite measurements and GNSS receivers under different space weather conditions. The project’s objective is to provide a reliable and efficient
solution to the challenges faced by low-Earth orbit satellite operators, particularly the unpredictability of solar activity that drives highly variable space weather.

MIT PIs:
Jaime Peraire, Assistant Professor,Department of Aeronautics and Astronautics

MIT Co-PIs:
Jordi Vila-Perez, Department of Aeronautics and Astronautics, Postdoctoral Associate
Ngoc-Cuong Nguyen, Department of Aeronautics and Astronautics, Principal Research Scientist

PT PIs:
Anna Morozova, Inst. de Astrofísica, U. of Coimbra, Researcher
Teresa Barata, Inst. de Astrofísica, U. of Coimbra, Researcher

Scientific Area: Earth Systems: Oceans to Near Space

Abstract: Proposed activities to extract vast critical mineral resources from the deep seabed, mostly in international waters, will generate sediment plumes whose impact on the marine environment is currently unclear. The size and settling velocity distributions of the suspended sediment are crucial parameters that govern the plume dynamics and extent, and in situ measurements of these parameters is essential due to the fragile and cohesive nature of marine sediment. The ENDLab research group at MIT has recently developed and tested a Real-Time Size and Settling Velocity (RTSSV) sensor for in situ measurements of sediment morphology and settling velocity distribution at abyssal depths. For this project, the MIT team will collaborate with the newly formed EU-funded TRIDENT program, which is being coordinated by INESC TEC. The objective is to develop and integrate the RTSSV with novel sensing platforms being developed by TRIDENT for real-time environmental monitoring.

MIT PIs:
Thomas Peacock, Professor,Department of Mechanical Engineering

PT PIs:
Eduardo Silva, Professor, INESC TEC

Scientific Area: Digital Transformation in Manufacturing

Abstract: Designing factories that efficiently deploy digitally controlled equipment such as robotics and 3D printing is essential to making manufacturing more efficient, sustainable, and locally-oriented. However, current design tools are not accessible to small and medium-sized manufacturers (SMMs) – which comprise
over 95% of the supply chain in most developed nations such as the U.S. and Portugal – and therefore many manufacturers lack the ability to make data-driven decisions to invest in automation.. The goal of
this project is to create a more capable and affordable factory simulation approach, integrating automated generation of simulation models with machine learning methods for generative design of factories, and so enable more resilient, distributed production networks. To properly incorporate principles of quality management, both at the factory and supply chain levels, we will collaborate with colleagues from U. Minho. We plan to apply this tool with companies in Portugal, as well as in
Massachusetts, and share the outcomes across the collaboration. The results will help guide manufacturers to make investments in advanced equipment that improves productivity and reduce their carbon footprint, and the tool will be further extensible to simulate highly distributed production systems within future cities or even in space.

MIT PIs:
John Hart, Professor,Department of Mechanical Engineering

PT PIs:
Paulo Sampaio, Professor de Qualidade e Excelência
Organizacional, University of Minho
Bárbara Rangel Carvalho, Assistant Professor, University of Porto

This grant is renewed until August 31, 2026

Scientific Area: Digital Transformation in Manufacturing

Abstract: Predictive models of solidification require atomic-scale resolution of the material structure and
chemistry. It is surprising then that none of the methodologies for computer-aided engineering
employed in industry account for the underlying atomistic nature of the solidification process. Here we
propose to develop a rigorous approach to integrate this atomic-scale information into practical
solidification models that can be used for additive manufacturing and welding-related applications. Our
goal is to elevate the predictive capabilities of these models such that the time-consuming process of
experimental trial and error employed in the development of new metallic alloys can be greatly reduced. 

MIT PIs:
Rodrigo Freitas, Assistant Professor,Department of Materials Science and Engineering

PT PIs:
João Pedro Oliveira, Assistant Professor Department of Materials Science Faculdade de Ciências e Tecnologia Universidade NOVA de Lisboa, Portugal

Scientific Area: Digital Transformation in Manufacturing

Abstract: Thanks to photovoltaic (PV) textiles, solar power can be harnessed in new ways such as in bags, cloths,
curtains, tents, sails, or construction tarps. However, the existing options for PV cells are either too expensive
or not scalable. Using inkjet printing, an affordable and widely used technique in the textile industry, we can fabricate PV cells on large textile surfaces. It would contribute to modernize Portuguese textile manufacturing and promote sustainability, or even eco-tourism. In this proposal, we outline the steps involved in our solution and how we plan to collaborate with academia and industry in Portugal to make this a reality. Ultimately, this project can participate in making Portugal a hub for high-tech innovation and revolutionize human-computer interfaces such as self powered smart sensing gloves for Augmented/Virtual Reality (AR / VR).

MIT PIs:
Joseph Paradiso, Professor,Program in Media Arts and Sciences

PT PIs:
Ana Baptista, FCT NOVA

Scientific Area: Sustainable Cities 

Abstract: In order to reach a carbon neutral building stock, building heating systems have to be electrified. However, system selection often happens later in the design process when key decisions have already been locked in. To address this issue, this research aims to develop new workflows to effectively integrate the selection of  heating ventilation and air- conditioning (HVAC) systems earlier into the design process. We will design and test a series of tools that enable architects to compare a variety of commercial HVAC systems and implement them into their projects during that phase. These new workflows will be tested in architectural education and practice alike.

MIT PIs:
Christoph Reinhart, Professor, Department of Architecture

PT PIs:
Paulo Ferrão, Professor of Instituto Superior Técnico – University of Lisbon, President of IN+, Center for Innovation, Technology and Policy Research.

Scientific Area: Earth Systems: Oceans to Near Space

Abstract: Semantic Simultaneous localization and mapping (SLAM) refers to the ability of a robot to build object-based models of the environment, accounting for uncertainty. To do so, a robot must combine continuous geometric information about its trajectory and object locations with discrete semantic information about object classes.  We are investigation several gaps in existing capabilities, including: (1) the ability to robustly estimate both the shape and the pose of objects, (2) the ability to transfer vision techniques. from terrestrial scenes to underwater scenes, and (3) the ability to create self-improving perception system for robots using semi-supervised machine learning, with location information from SLAM as a supervisory signal.

MIT PIs:
John Leonard, Professor, Department of Mechanical Engineering

PT PIs:
Nuno Alexandre Cruz, Senior researcher at the Ocean Systems Group, Lecturer at the Dept. of Electrical and Computer Engineering, FEUP, Porto, Portugal

This grant is renewed until August 31, 2026

Scientific Area: Digital Transformation in Manufacturing

Abstract: Hart’s research supported by the MIT-Portugal Program focuses on new computational tools and experimental techniques to explore the coupled design-material-economic capabilities of additive manufacturing (AM). In the past two years, his team has developed a physics-based throughput and cost modeling framework for AM, with application to laser-based techniques used widely in industry. Also, the team has invented a high-throughput workflow to predict the mechanical properties and processability of aluminum alloys for use in laser-based AM, and has demonstrated a new alloy composition that leverages metastable phases to retain superior strength than established aluminum alloys. In addition to these topics, the team has formed collaborations with researchers in Portugal focused on quality management in digital manufacturing, applications of AM to architecture and construction, and high-speed extrusion AM of polymer materials.

MIT PIs:
John Hart, Professor, Department of Mechanical Engineering

PT PIs:
Paulo Sampaio, Professor from University of Minho