Opportunities

Position Description 
We invite applications for a full-time Postdoctoral Research Fellow to join a massive research effort developing next-generation AI methods (foundation models) for healthy climate adaptation. The position will focus on building and evaluating foundation models for large-scale spatiotemporal health and environmental data. Our team leverages nationwide Medicare and Medicaid claims data the United States, linked with rich contextual information, including census, weather, and air pollution data. The overarching goal is to develop domain-specific foundation models that support tasks such as forecasting, interpolation/extrapolation, downscaling, and “what-if” scenario analysis relevant to climate-related health risks and adaptation strategies. 
 
Duties and Responsibilities  

• Design, implement, and evaluate deep learning models for spatiotemporal data, with an emphasis on medium-scale foundation models. 

• Leverage model embeddings in causal inference pipelines for health effects and adaptation policy evaluation. 

• Work with large, high-dimensional datasets (Medicare claims, census, weather, pollution, and related data), including data preprocessing, integration, and harmonization. 

• Lead and contribute to manuscripts for high-impact journals and conferences (e.g., Nature-like journals or top CS conferences). 

• Present findings in internal meetings and at national/international conferences. 

• Collaborate with an interdisciplinary team of biostatisticians, computer scientists, and climate scientists. 

• Contribute to open-source code, reproducible research workflows, and, where possible, public tools or model artifacts. 

Position Description 

We invite applications for a full-time Postdoctoral Research Fellow to join a massive research effort aimed at assessing the environmental and health impacts of AI data centers. The position will be  supervised by Professor Francesca Dominici and will focus on building and evaluating a decision framework to guide the expansion of AI data centers, aligning economic opportunity with social impact. Our team leverages data pipelines to quantify datacenters’ electricity and water use, emissions, and air pollution exposure and health impacts. The overarching goal is to develop an interactive utility-facing geospatial toolkit through data science and partnerships with grid operators.   
 
Duties and Responsibilities 

• Develop a scalable data science pipeline to harmonize and link detailed information on type, size, location of data centers in the US, their electricity and water demand, carbon emissions; exposure to air pollution. 

• Develop and/or apply methods for causal inference and machine learning  to estimate the excess number of adverse health events and directly attributable to data centers 

• Develop a decision-support platform that allows data center expansion while minimizing environmental exposures and associated health impacts. 

• Lead and contribute to manuscripts for high-impact journals and conferences (e.g., Nature-like journals or top CS conferences). 

• Present findings in internal meetings and at national/international conferences. 

• Collaborate with an interdisciplinary team of biostatisticians, computer scientists, climate scientists and community and  industry partners. 

• Contribute to open-source code, reproducible research workflows, and, where possible, public tools or model artifacts. 

Position Description 
We invite applications for a full-time Postdoctoral Research Fellow to join the causal inference team supervised by Professor Francesca Dominici. The position will focus on developing and applying novel causal inference methods for large-scale observational studies, with a particular emphasis on environmental exposures and public health. Core data resources include nationwide claims, linked with rich contextual information such as census data, weather records, and high-resolution air pollution and related environmental exposures data. 
Motivated by relevant public health and policy questions, the goal is to develop methodologies for the identification, estimation, transportability, and generalization of the causal effects in complex real-world settings. Among others, methodological areas will span  

• causal inference for spatiotemporal data, 

• methods for heterogeneous treatment effects estimation, 

• methods for multiple exposures, multiple outcomes, 

• ML and AI methods for causal inference, 

• Bayesian causal inference, 

• methods for transportability and generalizability of causal effects across space, time, and populations. 

Duties and Responsibilities  

• Design, develop and implement novel causal inference methods in the areas listed in the position description. 

• Work with large, high-dimensional datasets. 

• Lead and contribute to manuscripts for high-impact journals (e.g., top Statistics journals and Nature-like journals). 

• Present findings in internal meetings and at national/international conferences. 

• Collaborate with an interdisciplinary team (bio)statisticians, data scientists, computer scientists, and climate scientists.  

• Contribute to open-source code and reproducible pipelines.