A cover letter is required for consideration for this position and should be attached as the first page of your resume. The cover letter should address your specific interest in the position and outline skills and experience that directly relate to this position.

Should you have any questions regarding the application process, please contact the sponsoring faculty of this position, Dr. Tiantian Yang, at Dr. Yang's current email Tiantian.Yang@ou.edu  (primary contact before Dec 31, 2025), with a carbon copy to Dr. Yang's University of Michigan email address yangtt@umich.edu (primary contact after Jan 1st, 2026).

The University of Michigan's School for Environment and Sustainability (SEAS) and Dr. Tiantian Yang's research group are seeking a highly motivated Postdoctoral Research Fellow in Artificial Intelligence (AI) and Deep Learning (DL) for Extreme Weather Prediction, Climate Adaptation, Flood Forecasting, and Water Resources Planning. The successful candidate will conduct cutting-edge research at the intersection of AI/DL modeling, hydrological sciences, and weather/climate sciences, with a focus on applications to extreme weather forecasting (hurricanes, severe storms, floods, and water-related natural hazards), climate adaptation, and streamflow/flood forecasting from watershed to global scales.
 

This position will involve developing, applying, and benchmarking advanced AI/DL architectures (e.g., physically-informed AI/DL models, state-space models, spatiotemporal transformers, diffusion models, foundation models and/or Large Language Processing Models) to improve prediction and decision support for weather extremes impacts and water and energy resources planning. The postdoc will collaborate with interdisciplinary teams across SEAS, climate and weather centers, agency partners, etc., and will contribute to advancing climate resilience and sustainable water management.
 

Additional responsibilities include mentoring graduate students, co-authoring high-impact publications, contributing to competitive grant proposals, engaging with agency and community partners, and presenting research at major conferences.

At the University of Michigan School for Environment and Sustainability (SEAS), we are at the forefront of building a more sustainable and just world for all by transforming the impact of higher education and reimagining the future. We are advancing action through innovation, research, education and engagement in society, and developing leaders who are empowered to halt the climate crisis and create an environmentally sound future for generations to come.
 

To learn more about SEAS and our values, please visit our website at https://seas.umich.edu/about/seas-values. 

In addition to a career filled with purpose and opportunity, The University of Michigan offers a comprehensive benefits package to help you stay well, protect yourself and your family and plan for a secure future. Benefits include:

• Generous time off
• A retirement plan that provides two-for-one matching contributions with immediate vesting
• Many choices for comprehensive health insurance
• Life insurance
• Long-term disability coverage
• Flexible spending accounts for healthcare and dependent care expenses

• 30% -Conduct research on AI/DL-based extreme weather forecasting, climate modeling, hydrologic forecasting, and flood prediction at watershed to global scales.
• 30% -Develop research about the integration of physically-informed AI/DL models with climate and hydrologic datasets (e.g., reanalysis, satellite, and observational networks).
• 20% -Prepare high-quality publications, present results at leading conferences, and contribute to interdisciplinary workshops.
• 15% -Contribute to grant proposals and collaborative research project development.
• 5% -Mentor graduate students and engage in community/agency outreach and stakeholder engagement.

• Expertise in one or more of the following DL architectures: Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), State-Space Models, Diffusion Models, Transformers, and/or Graph Neural Networks (GNNs).
• Knowledge of Physically-Informed or Physically-Aware AI/DL Architectures, i.e., machine learning models explicitly constrained by physical laws (e.g., conservation of mass, momentum, or energy) or designed to integrate physics-based models and data-driven learning for improved interpretability and generalization.
• Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters.
• Demonstrated ability to work collaboratively in interdisciplinary and cross-agency teams.

Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes.

Term Appointment Language
 

The initial appointment for this position will be for a one-year term (full-time). This appointment is a limited appointment with the possibility of renewal, subject to performance and funding availability.

Salary will be determined based on experience.

Job openings are posted for a minimum of seven calendar days.  The review and selection process may begin as early as the eighth day after posting. This opening may be removed from posting boards and filled anytime after the minimum posting period has ended.

The University of Michigan is an equal employment opportunity employer.