Postdoctoral Research Position in Data Science/ML for Assessing Societal Impacts of AI Data Centers

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 data centers' 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.

Basic Qualifications:

• PhD (completed or near completion) in one of the following or a closely related field:
  • Computer Science
  • Statistics / Biostatistics
  • Applied Mathematics
  • Data Science
• Demonstrated expertise in modern machine learning, including at least one of the following:
  • Spatiotemporal modeling or geospatial/temporal data analysis
  • Causal inference
• Strong programming skills in Python and experience with PyTorch, required to have experience developing code with a team through collaborative version control
• Experience working with large datasets and cloud computing environments.
• Solid background in statistical modeling and inference
• Excellent written and oral communication skills, with a track record of peer-reviewed publications commensurate with career stage.

Additional Qualifications:

Prior experience with one or more of:

• Health claims data, EHRs, or other large-scale health/administrative datasets
• Environmental, climate, or air pollution exposure data
• Causal inference methods

Familiarity with interdisciplinary work at the interface of computer science, climate, environment, and health.