#R46513
ability to reduce algorithms and theoretical knowledge to practice and produce innovative research results
Demonstrated programming proficiency in Python/C++.
Familiarity with machine learning frameworks such as PyTorch, TensorFlow, Julia.
Strong computer science background is a plus.
Familiarity with recent research trends in physics-informed machine learning e.g. physics-informed neural networks, neural operator theory, DeepONets is a plus.
Exposure to one or more application areas in scientific computing (computational electromagnetics, fluid dynamics, molecular dynamics, thermal analysis, electrical circuit simulation) and/or computational physics is a plus.
Candidate should expect to work with a cross-functional engineering team to perform cutting-edge research but ultimately deliver innovative technologies in a production environment.
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