Postdoctoral Scholar - Chattopadhyay Lab

The Department of Applied Mathematics at the University of California, Santa Cruz (UCSC) invites applications for postdoctoral scholars. Under the direction of Assistant Professor Ashesh Chattopadhyay, the postdoctoral scholar will be responsible for working on the proposed tasks of a recently funded NSF project on building physically-consistent AI emulators for the climate system.

Applicants with expertise in building large-scale deep learning models and experience in handling Earth system model outputs or reanalysis data (ERA5, NCEP) or previous experience with training scientific machine learning models on physical data are particularly encouraged to apply.

APPLICATION REQUIREMENTS

All documents and materials must be submitted as PDFs and should be forwarded to Ashesh Chattopadhyay at aschatto@ucsc.edu.

Please refer to Position #JPF01844 in all correspondence.

Documents/Materials

• Letter of application that briefly summarizes your qualifications and interest in the position (required).
• Curriculum vitae (required).
• Two peer-reviewed published articles that the applicant has authored (required).
• List of professional references (3-4) that includes their contact information; only references of those applicants who are under serious consideration will be contacted (required).
• Statement of contributions to diversity, equity, and inclusion: Statement addressing your understanding of the barriers facing traditionally underrepresented groups and your past and/or future contributions to diversity, equity, and inclusion through teaching and professional or public service. Candidates are urged to review guidelines on statements (see https://apo.ucsc.edu/diversity.html) before preparing their application (optional).

Theoretical and Applied Complex Systems Lab: https://sites.google.com/view/ashesh6810/home

Applied Mathematics Department: https://engineering.ucsc.edu/departments/applied-mathematics/

• Numerical and programming skills.
• Experience with machine learning models.
• Experience running models on HPC infrastructure.
• Experience dealing with either fluid dynamics and/or large complex climate datasets.

• Ph.D. (or equivalent foreign degree) in atmospheric, oceanic, or earth sciences; mechanical engineering; applied mathematics; computer science; or a related discipline.
  
  

• Dealing with global climate model outputs
• Ability to write pytorch or JAX models and run them
• Training or running AI weather models like FourCastNet, GraphCast, Fuxi, etc
• Some understanding of atmospheric physics and dynamics

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