Post Doc Res Assoc

University of Utah • Salt Lake City, UT

36d • $60,000 - $70,000

About The Position

The Kahlert School of Computing at the University of Utah invites applications for one a 1-year post-doctoral researcher for interdisciplinary work spanning machine learning, applied mathematics, computer science, and material sciences, with a heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites and nonlinear materials, specifically dry fibers, and will work closely with the Air Force Research Laboratory (AFRL) and ARCTOS to integrate research into an existing JAX FEM framework jointly developed by University of Utah, AFRL, and ARCTOS. This position also carries an expectation that any candidate will spend 9 months of the academic year at the University of Utah under the supervision of Professors Varun Shankar and Robert M. Kirby, and 3 months on-base at AFRL working with ARCTOS/AFRL while continuing collaboration with the University of Utah. Successful candidates will have the opportunity to continue working with ARCTOS as a research scientist to further this and other lines of research. Salary Range: $60K - $70K - Post-doc funding depending on experience, with additional benefits.

Requirements

• The Kahlert School of Computing is seeking a highly talented and committed individual with a demonstrated ability to work well with minimal supervision in a multi-disciplinary research environment.
• Backgrounds in the engineering sciences, applied mathematics, physics and computational sciences will be considered.
• Being self-motivated and having good organizational, communication, and teamwork skills is essential.
• U.S. Citizenship is required.

Nice To Haves

• Individuals comfortable with machine learning, the JAX library, finite element methods, and/or high-performance computing are preferred; material science knowledge/expertise is a bonus.
• We are particularly looking for candidates interested in a long-term position with ARCTOS after the conclusion of this 1-year position, though this is not a strict requirement.

Responsibilities

• Contribute to the development of a hybrid software framework for the finite element method and machine learning within the JAX Python library, including efficient implementations of classical numerical algorithms.
• Extend the hybrid FEA-ML framework to include nonlinear cohesive zone models with simple traction separation laws in a modular manner such that additional traction-separation laws can easily be incorporated in the future.
• Further extend the framework towards allowing discrete damage modeling via extended finite element methods, leveraging existing and actively developed libraries in AFRL related to crack growth.
• Employ the hybrid FEM-ML framework to demonstrate a machine-learned crack model based on rich experimental data, using material information below the resolution of the mesh and the stress field as predicted by an FEA solution as inputs and whether a crack should initiate or the direction that an existing crack should extend as outputs.
• Contribute to the hybrid FEA-ML framework in the JAX Python library to support the simulation of dry-fiber mechanics that incorporates the relevant physics and contact, as identified by AFRL through recent past efforts. This includes the implementation of relevant algorithms and solvers for distributed GPU computing within the JAX Python library.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Industry: Educational Services
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees