Post Doctoral Scholar - AI and Machine Learning

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.

• 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 of offer.
• 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., * 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., Employment with the University will require successful completion of background check(s) in accordance with University policies.

Penn State provides a competitive benefits package for full-time employees designed to support both personal and professional well-being.

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