About this role
Summary: Develops and applies global AI/ML Earth system emulators and regional downscaling techniques to produce large ensembles of extreme weather scenarios for water and energy risk assessment.
APPLICATION INSTRUCTIONS:
- ## CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please [login to Workday](https://www.myworkday.com/psu/d/inst/13102!CK5mGhIKBggDEMenAhIICgYI1A0Q1QI~*qyZ5PNog5Ug~/cacheable-task/2998$46522.htmld#backheader=true) to complete the [internal application process](https://pennstateoffice365.sharepoint.com/:b:/s/KnowledgeBaseArticlesSharing/EfiwgKReZ4VAjprFZPKWUm0Bq_sU3KzTmERfpryq0esPKQ). Please do not apply here, apply internally through Workday.
- ## CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please [login to Workday](https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.myworkday.com%2Fpsu%2Fd%2Finst%2F13102!CK5mGhIKBggDEMenAhIICgYI1A0QkwE~%2Fcacheable-task%2F23748%245.htmld%23TABINDEX%3D0&data=04%7C01%7Crkh108%40psu.edu%7C4eae4f0ec9854dd03b6808d9663d0c90%7C7cf48d453ddb4389a9c1c115526eb52e%7C0%7C0%7C637653234648637442%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=wChxz3SXc%2F99%2BgXQFsovztGrTOzodVW2mRVTZHPgZrA%3D&reserved=0) to complete the [student application process.](https://pennstateoffice365.sharepoint.com/:b:/s/KnowledgeBaseArticlesSharing/Eeys15Xva4hFokHVtbP1eGIB5i1WFLc-J3k9KHgrFYZ7mg) Please do not apply here, apply internally through Workday.
- ## If you are NOT a current employee or student, please click “Apply” and complete [the application process for external applicants](https://hr.psu.edu/sites/hr/files/Application_Process_for_External_Applications_Non-Penn_State_Employees_.pdf).
Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see [Notice to Out of State Applicants](https://hr.psu.edu/current-employee/flexible-work-arrangements/out-of-state-notice?utm_source=workday&utm_medium=req&utm_campaign=Oct23&utm_id=out-of-state).
This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding.
POSITION SPECIFICS
The Department of Earth and Mineral Sciences at The Pennsylvania State University invites applications for a full-time Postdoctoral Scholar to support the DOE-funded HyperFORMANCE project. The successful candidate will work under the supervision of Prof. Colin Zarzycki and contribute to a multi-institutional team. The postdoc will play a central role in developing and applying global AI/ML Earth system emulators and regional downscaling techniques to generate large ensembles of physically credible extreme weather scenarios for water and energy system risk assessment.
RESEARCH FOCUS
The postdoc's work will include:
- Evaluate where global AI/ML emulators (ACE-E3SM, FourCastNet, NeuralGCM) credibly reproduce extreme weather characteristics and where physics-based models remain necessary
- Combine physics-based simulations, existing model archives, and certified AI/ML emulation to build libraries of decision-relevant extreme events
- Develop and apply machine learning clustering techniques to distill large event libraries into representative scenario sets and event archetypes
- Generate large ensembles of spatially coherent, temporally compounded hazard scenarios at testbed-relevant scales
- Work with hydrologic, energy, and urban impacts teams to ensure scenarios are suitable for downstream applications
KEY RESPONSIBILITIES
AI/ML Model Application and Evaluation
- Deploy and analyze outputs from global AI/ML weather and climate emulators, including multi-decadal free-running simulations
- Apply process-oriented diagnostics (e.g., TempestExtremes) to evaluate emulator fidelity for extreme precipitation, atmospheric rivers, tropical cyclones, and extratropical cyclones
- Compare AI emulator outputs against physics-based benchmarks (E3SM, SCREAM) and observations
- Apply regional AI/ML downscaling tools (CorrDiff, StormCast-WUS) to generate km-scale meteorological fields suitable for impacts modeling
- Contribute to development and refinement of downscaling workflows across multiple testbed regions
- Identify representative event archetypes spanning the distribution of extreme outcomes
- Contribute to peer-reviewed publications documenting emulator evaluation, downscaling fidelity, and scenario methodology
- Present results at scientific conferences (AGU, AMS)
- Collaborate with multi-institutional partners on scenario development and evaluation
- Participate in project workshops and cross-team coordination meetings
REQUIRED QUALIFICATIONS
- Ph.D. in atmospheric science, Earth science, computer science, physics, applied mathematics, or closely related field, conferred prior to the start date
- Demonstrated experience with scientific Python programming (NumPy, Xarray, Pandas, Scikit-learn)
- Experience with machine learning techniques (clustering, classification, dimensionality reduction, or emulation)
- Familiarity with large climate or weather datasets and analysis of extreme events
- Experience in UNIX and high-performance computing environments
- Strong written and verbal communication skills
- Ability to work collaboratively in a multi-institutional team environment
PREFERRED QUALIFICATIONS
- Experience with physics-based and/or data-driven climate/weather models (E3SM, CESM, SCREAM, ACE2, FourCastNet, NeuralGCM, StormCast, or similar)
- Familiarity with feature-based extreme event detection and tracking
- Knowledge of tropical cyclones, atmospheric rivers, mid-latitude storms and/or other synoptic-scale weather extremes
- Interest in uncertainty quantification and ensemble modeling
APPLICATION MATERIALS
Applicants should submit the following materials:
- Current curriculum vitae
- Detailed statement of research interests and relevant experience (1-2 pages), addressing how your background aligns with the postdoc responsibilities
- Contact information for three professional references
BACKGROUND CHECKS/CLEARANCES
Employment with the University will require successful completion of background check(s) in accordance with University policies.
BENEFITS
Penn State provides a competitive benefits package for full-time employees designed to support both personal and professional well-being.
For more detailed information, please visit our [Benefits Page.](https://hr.psu.edu/current-employee/benefits) (Note: For Postdoctoral benefits, please see our [Postdoctoral Benefits](https://hr.psu.edu/current-employee/benefits/post-doc-appts) page.)
CAMPUS SECURITY CRIME STATISTICS
Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review [here](https://police.psu.edu/annual-security-reports).
EEO IS THE LAW
Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814-865-1473.
Penn State is committed to and accountable for advancing equity, respect, and belonging. We embrace individual uniqueness, as well as a culture of belonging that supports equity initiatives, leverages the educational and institutional benefits of inclusion in society, and provides opportunities for engagement intended to help all members of the community thrive. We value belonging as a core strength and an essential element of the university’s teaching, research, and service mission.
[Federal Contractors Labor Law Poster](https://affirmativeaction.psu.edu/files/2022/01/Federal-Contractors-Labor-Law-Poster.pdf)
[PA State Labor Law Poster](https://affirmativeaction.psu.edu/files/2022/01/PA-State-Labor-Law-Poster.pdf)
[Penn State Policies](https://policy.psu.edu/)
[Copyright Information](https://www.psu.edu/copyright-information)
[Hotlines](https://universityethics.psu.edu/reporting-at-penn-state)