Postdoc: AI for Climate-Health Adaptation & Data Science

Harvard University; Boston (MA); Híbrido; USD 67.500 - 82.500; Tempo integral

Resumo da vaga

Harvard University seeks a Postdoctoral Research Fellow focusing on developing AI methods for healthy climate adaptation in the National Studies on Air Pollution and Health group. The role involves designing deep learning models for large-scale health data and requires a PhD in related fields. The ideal candidate should have strong programming skills, experience with machine learning, and a collaborative spirit. Salary for the position is around $75,000, and applications must include a cover letter and CV.

Qualificações

• Demonstrated expertise in modern machine learning.
• Expertise in deep learning models like transformers.
• Experience with large datasets.

Responsabilidades

• Design and evaluate deep learning models for spatiotemporal data.
• Lead and contribute to manuscripts for high-impact journals.
• Collaborate with an interdisciplinary team.

Conhecimentos

Deep learning

Spatiotemporal modeling

Programming in Python

Statistical modeling and inference

Formação

PhD in Computer Science, Statistics, Applied Mathematics, or Data Science

Ferramentas

PyTorch

Cloud computing environments

Descrição da vaga

Postdoctoral Research Position in AI for Healthy Climate Adaptation

Title Postdoctoral Research Position in AI for Healthy Climate Adaptation

School

School Harvard T.H. Chan School of Public Health

Department/Area

Position Description

Position Description
The National Studies on Air Pollution and Health ( NSAPH ) group, led by Prof. Francesca Dominici, invites applications for a full-time Postdoctoral Research Fellow to join a massive research effort developing next-generation AI methods 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 claims data for older adults in 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.

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:
  • Deep learning (e.g., transformers, sequence models, representation learning)
  • Spatiotemporal modeling or geospatial/temporal data analysis
  • Medium-to-Large-scale foundation models pretraining/fine-tuning paradigms
• 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
• Causal inference methods
• Uncertainty quantification and model calibration for decision-making
• Familiarity with interdisciplinary work at the interface of climate, environment, and health.

Special Instructions

Please submit the following materials:

• Cover letter describing your research interests, relevant experience, and fit for this position.
• Curriculum vitae including a list of publications.
• One to three representative publications or preprints.
• Names and contact information for 2–3 references.

Contact Information

Catherine Adcock

Contact Email

Contact Email [e-mail]

Salary Range

$75,000

Minimum Number of References Required

Minimum Number of References Required 2

Maximum Number of References Allowed

Maximum Number of References Allowed 3

Keywords

biostatistics; artificial intelligence; climate science

EEO/Non-Discrimination Commitment Statement

Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes.

Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university’s non-discrimination policy . Harvard’s equal employment opportunity policy and non-discrimination policy help all community members participate fully in work and campus life free from harassment and discrimination.

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