SULI - MCS - Schlittgen-Li, Milo - 2.24.26.
About The Position
This project aims to develop a learned, data-driven surrogate for nonlinear or optimization-based conservative field remapping, targeting computational bottlenecks in the Energy Exascale Earth System Model (E3SM) workflows. The student will help train a mesh-informed Graph Neural Network to approximate high-order, coarse-to-fine mappings between finite-volume (FV) and FV or spectral-element (SE) grids, which presently require complex nonlinear filters to remove grid imprinting and enforce physical constraints. The student will help generate training data and train the model.
Requirements
- The entirety of the appointment must be conducted within the United States.
- Applicants must be: Currently enrolled in undergraduate or graduate studies at an accredited institution. Graduated from an accredited institution within the past 3 months; or Actively enrolled in a graduate program at an accredited institution.
- Must be 18 years or older at the time the appointment begins.
- Must possess a cumulative GPA of 3.0 on a 4.0 scale.
- Must be a U.S. citizen or Legal Permanent Resident at the time of application.
- If accepting an offer, must pass a screening drug test.
Responsibilities
- help generate training data
- train the model
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
No Education Listed
