Postdoctoral Scholar-Global AI/ML Modeling and Scenario Generation

The Pennsylvania State University; State College (Centre County); On-site; USD 55,000 - 75,000; Full time

Job summary

The Pennsylvania State University’s Department of Earth and Mineral Sciences invites applications for a full-time Postdoctoral Scholar to support the DOE-funded HyperFORMANCE project under Prof. Colin Zarzycki.

The successful candidate will help develop and apply global AI/ML Earth system emulators and regional downscaling to generate large ensembles of physically credible extreme weather scenarios for water and energy risk assessment.

Qualifications

• PhD in atmospheric/earth science, computer science, physics, math or closely related field, conferred before start date.
• Experience with scientific Python (NumPy, Xarray, Pandas, Scikit-learn).
• Experience with ML techniques (clustering, classification, dimensionality reduction, emulation).
• Familiar with large climate/weather datasets and extreme-event analysis.
• Experience in UNIX and high-performance computing environments.
• Strong written and verbal communication and teamwork.

Responsibilities

• Deploy and analyze outputs from global AI/ML weather emulators and multi-decadal simulations.
• Apply diagnostics to evaluate emulator fidelity for extreme precipitation and cyclones.
• Compare AI emulator outputs with physics-based benchmarks and observations.
• Apply regional downscaling tools to generate km-scale meteorological fields for impacts modelling.
• Develop and refine downscaling workflows across testbed regions.
• Identify representative event archetypes and contribute to publications.
• Present results at scientific conferences and collaborate with multi-institution partners.

Skills

Scientific Python

Machine learning

Climate data analysis

Unix / HPC

Effective communication

Education

PhD in atmospheric science or related field

Job description

POSITION SPECIFICS

This is a term position; length of the term will be discussed during the interview process. Continuation past the termlengthdiscussed willbebasedonuniversityneed,performance,and/oravailabilityoffunding.

Approval of remote and hybrid work is not guaranteed regardless of work location.For additional information on remote work at Penn State, seeNotice to Out of State Applicants.

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

• 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

• 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 ourBenefits Page. (Note: For Postdoctoral benefits, please see our Postdoctoral Benefits 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.

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.

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