Postdoc: Global AI/ML Weather Scenario Modeling

Penn State; Pennsylvania; Sur place; USD 55 000 - 75 000; Plein temps

Résumé du poste

The Pennsylvania State University invites applications for a full-time Postdoctoral Scholar to advance global AI/ML Earth system emulators and regional downscaling for large ensembles of extreme weather scenarios. You will work with a diverse team across Earth and energy systems to inform water and energy risk assessments.

Required qualifications include a Ph.D. in a related field, strong Python and ML skills, and experience with large datasets and HPC.

Qualifications

• Ph.D. in a closely related field conferred prior to start date.
• Demonstrated experience with scientific Python (NumPy, SciPy) and ML methods.
• Experience with large climate/weather datasets and HPC environments.
• Strong written and verbal communication skills; ability to collaborate across institutions.

Responsabilités

• Deploy and analyze outputs from global AI/ML weather and climate emulators.
• Apply diagnostics to evaluate emulator fidelity and compare with physics-based benchmarks.
• Develop downscaling workflows and distill event archetypes for scenario libraries.
• Collaborate with multi-institution partners and contribute to publications.

Connaissances

Scientific Python programming

ML techniques (clustering, etc.)

Large climate/weather datasets

Strong communication

Multi-institution collaboration

Formation

Ph.D. in atmospheric science / Earth science / CS / physics / applied math

Outils

NumPy

Xarray

Pandas

Scikit-learn

UNIX

HPC

Description du poste

The Pennsylvania State University invites applications for a full-time Postdoctoral Scholar to advance global AI/ML Earth system emulators and regional downscaling for large ensembles of extreme weather scenarios. You will work with a diverse team across Earth and energy systems to inform water and energy risk assessments.

Required qualifications include a Ph.D. in a related field, strong Python and ML skills, and experience with large datasets and HPC.