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Full Time Postdoctoral Math Jobs (NOW HIRING)

Berkely Lab

Postdoctoral Scholar

Berkeley, CA

Postdoctoral Fellow Berkeley Lab's Center for Advanced Mathematics for Energy Research Applications ... This is a full-time, 2 year, postdoctoral appointment with the possibility of renewal based upon ...

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How much do full time postdoctoral math jobs pay per year?

As of May 28, 2026, the average yearly pay for full time postdoctoral math in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full Time Postdoctoral Math researcher, and why are they important?

To thrive as a Full Time Postdoctoral Math researcher, you need an advanced degree (typically a PhD in Mathematics), strong analytical skills, and a robust research background in your mathematical field. Experience with programming languages (such as Python or MATLAB), mathematical software (like LaTeX or Mathematica), and a solid publication record are often expected. Exceptional problem-solving abilities, clear scientific communication, and effective collaboration make candidates stand out. These skills and qualities are crucial for producing impactful research, publishing in top journals, and contributing to academic and interdisciplinary teams.

What are some common challenges faced by full-time postdoctoral math researchers, and how can they be addressed?

Full-time postdoctoral math researchers often encounter challenges such as balancing independent research with collaborative projects and managing the pressure to publish regularly. Navigating expectations from both supervisors and funding agencies can also be demanding. Building a strong professional network and seeking mentorship can help overcome these challenges, while effective time management and clear goal-setting are essential for maintaining research productivity and career progression.

What are full time postdoctoral math positions?

Full time postdoctoral math positions are temporary research roles typically held by individuals who have recently earned a Ph.D. in mathematics or a related field. These positions allow researchers to further develop their expertise, work on advanced mathematical projects, and publish scholarly articles under the mentorship of senior faculty. The roles are usually full-time, lasting from one to three years, and can serve as a stepping stone to academic careers or advanced research positions in industry.

What is the difference between Full Time Postdoctoral Math vs Research Scientist?

AspectFull Time Postdoctoral MathResearch Scientist
Required CredentialsPhD in Mathematics or related fieldMaster's or PhD in relevant field, often with specialized expertise
Work EnvironmentAcademic institutions, universities, research labsResearch organizations, industry labs, corporate R&D
Employer & Industry UsagePrimarily academia, government researchIndustry sectors like tech, finance, pharmaceuticals
Common Search & ComparisonYesYes

Full Time Postdoctoral Math roles focus on academic research and teaching, often in universities, requiring a PhD. Research Scientist positions are more industry-oriented, involving applied research in corporate or government settings, with a broader range of qualifications. Both roles involve research but differ mainly in environment and application focus.

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Postdoctoral Research Position in AI for Healthy Climate Adaptation

Postdoctoral Research Position in AI for Healthy Climate Adaptation

Harvard University

Cambridge, MA • On-site

$75K/yr

Full-time

Posted 15 days ago

Harvard University rating

8.1

Company rating: 8.1 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

128th of 528 rated colleges and universities

Job description

Position
Details
Title
Postdoctoral Research Position in AI for Healthy Climate Adaptation
School
Harvard T.H. Chan School of Public Health
Department/Area
Biostatistics
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
  • Environmental, climate, or air pollution exposure data
  • 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
catherine_adcock@harvard.edu
Salary Range
$75,000
Minimum Number of References Required
2
Maximum Number of References Allowed
3
Keywords
biostatistics; artificial intelligence; climate science

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