Sort by
Refine Your Search
-
Listed
-
Employer
- Carnegie Mellon University
- Oak Ridge National Laboratory
- Argonne
- Texas A&M University
- University of Maryland, Baltimore
- University of Utah
- Massachusetts Institute of Technology
- New York University
- Northeastern University
- SUNY Polytechnic Institute
- TTI
- University of Houston
- University of Miami
- University of Minnesota
- University of North Texas at Dallas
- Virginia Tech
- 6 more »
- « less
-
Field
-
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
-
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
-
structure-preserving discretization algorithms (a refinement of finite-element analysis compatible with exact geometric, topological, and physical constraints) with artificial neural networks for achieving
-
. Ability to perform finite element simulations using software such as COMSOL, ANSYS, or ABAQUS. Experience in utilizing these tools for in-depth analysis is highly desirable. Required License/Registration
-
to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
-
finite element analysis and modal analysis techniques. • Experience with vibration analysis, dynamic testing, or mechanical systems characterization. • Proven record of publishing refereed journal articles
-
computational mechanics of structures. Strong foundation in shell theory, elasticity, and variational mechanics Experience developing custom numerical solvers or using advanced finite element platforms (e.g
-
working in radiological environment. Experience in heat transfer and thermal modeling/simulation using finite-element analysis (FEA) or other software. Ability to work within a multi-disciplinary team