Postdoctoral Scholar-Global AI​/ML Modeling and Scenario Generation

Postdoctoral Scholar-Global AI/ML Modeling and Scenario Generation### Postdoctoral Scholar-Global AI/ML Modeling and Scenario Generation Apply:
Penn State University Park:
Full time:
Posted Yesterday:
REQ #
** APPLICATION INSTRUCTIONS:*** ## CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process. 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 to complete the student application process. 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.
** 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.
** 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, Four Cast Net , 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., Tempest Extremes) 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 (Corr Diff, Storm Cast-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.…