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APPLICATION INSTRUCTIONS:

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• 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, seeNotice to Out of State Applicants.

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

POSITION SPECIFICS

The Department of Meteorology and Atmospheric Science and Institute for Computational and Data Sciences (ICDS) at Penn State is seeking a postdoctoral scholar in the area of artificial intelligence (AI) and machine learning (ML) applied to numerical weather prediction and data assimilation. In particular, the postdoc would be part of the team spanning NASA Goddard, Purdue University, Penn State University, and others to support the project "Machine-Learning to Improve Cycling and Forecasts with GEOS and Expedite the Evaluation of Assimilating Observations from New Instruments," supported by the NASA AIST program. The project involves the training, tuning, and evaluation of computer vision based ML surrogate models for the atmosphere and sea surface as part of the ensemble component of the NASA GEOS hybrid ensemble data assimilation system. Evaluations based on principles from machine learning, data assimilation, predictability, and atmospheric and oceanic phenomena on high performance computing (HPC) environments will be important for the project. Explorations of additional ways to hybridize data assimilation and machine learning are possible. The postdoc would join a cohort of AI/HPC postdocs affiliated with ICDS, which would provide opportunities to engage in an interdisciplinary community and interact with the different faculty co-hires and researchers in the institute applying these techniques to various disciplines.

Required Qualifications

• A Ph.D. in a discipline related to this work, including Meteorology and Atmospheric Science, Computer Science, Engineering, Mathematics, Statistics is required by the start date. Must provide proof of a scheduled dissertation defense date for a PhD by the time ofoffer.
• Strong computer programming skills and the ability to work independently on complex problems.
• Expertise in applying and evaluating data assimilation and/or machine learning techniques.
• Ability to work effectively as part of a team, with strong written and oral communication skills, and motivation and ability to meet project timelines.

Preferred Qualifications

• Previous experience training and evaluating deep learning emulators for high dimensional geophysical systems.
• Previous experience using ensemble and hybrid variational data assimilation systems.
• Expertise with the predictability of atmospheric and earth system predictions.

Preferred Start Date: June 1, 2026

The position would be for one year, with the possibility of renewal for a second year pending good performance and availability of funds.

Application Instructions

Interested candidates should submit a cover letter describing their interest in the position, a CV, and names & contact information of up to three references.

Questions regarding the position may be directed to Dr. Steven Greybush, sjg213@psu.edu.

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.)

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