Postdoctoral Scholar-Global AI/ML Modeling and Scenario Generation

The Pennsylvania State University; University Park (TX); On-site; USD 60,000 - 75,000; Full time

Job summary

The Pennsylvania State University invites applications for a full-time Postdoctoral Scholar to advance the HyperFORMANCE project. You will contribute to a multi-institutional team, developing global AI/ML earth system emulators and regional downscaling for large ensembles of extreme weather scenarios.

Working under Prof. Colin Zarzycki, you will deploy emulators, evaluate fidelity against physics-based benchmarks, and participate in publications and conferences.

Qualifications

• Ph.D. in atmospheric science, Earth science, computer science, physics, applied mathematics, or a 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.

Responsibilities

• Deploy and analyze outputs from global AI/ML weather and climate emulators, including multi-decadal free-running simulations.
• Apply process-oriented diagnostics to evaluate emulator fidelity for extreme precipitation and cyclones.
• Compare emulator outputs against physics-based benchmarks and observations.
• Apply regional AI/ML downscaling tools to generate km-scale meteorological fields for impacts modeling.
• Contribute to development and refinement of downscaling workflows across testbed regions.
• Identify representative event archetypes spanning the distribution of extreme outcomes.
• Contribute to peer-reviewed publications documenting evaluator methods and scenarios.
• Present results at conferences (AGU, AMS).
• Collaborate with multi-institutional partners on scenario development and evaluation.
• Participate in project workshops and cross-team coordination meetings.

Skills

Scientific Python programming

Machine learning techniques

UNIX & HPC environments

Communication skills

Team collaboration

Extreme events analysis

Education

Ph.D. in atmospheric science / earth science / computer science / physics / applied mathematics

Job description

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

• 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

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

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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