Research Aide - XSD - Popryho, Leonid - 4.7.26
About The Position
Physics-Informed Surrogate Modeling for Cryogenic Systems We are seeking a motivated PhD student to develop physics-informed neural network (PINN) and graph neural network (GNN) surrogate models for cryogenic systems. This work is part of a multi-institutional project on AI-enabled co-design for low-temperature electronics. Generate training datasets using FEM simulations. Implement sim-to-sim transfer learning strategies. Benchmark surrogate accuracy and speedup against conventional coupled simulation workflows. Validate surrogate predictions against experimental cryogenic data. Education and Experience 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. If accepting an offer, candidates may be required to complete pre-employment drug testing based on appointment length. All students remain subject to applicable drug testing policies. Must complete a satisfactory background check.
Requirements
- The entirety of the appointment must be conducted within the United States.
- 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.
- If accepting an offer, candidates may be required to complete pre-employment drug testing based on appointment length. All students remain subject to applicable drug testing policies.
- Must complete a satisfactory background check.
Responsibilities
- Develop physics-informed neural network (PINN) and graph neural network (GNN) surrogate models for cryogenic systems.
- Generate training datasets using FEM simulations.
- Implement sim-to-sim transfer learning strategies.
- Benchmark surrogate accuracy and speedup against conventional coupled simulation workflows.
- Validate surrogate predictions against experimental cryogenic data.
Benefits
- comprehensive benefits are part of the total rewards package.
- Click here to view Argonne employee benefits!
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
No Education Listed
Number of Employees
1,001-5,000 employees
