Cadence Design Systems is a world leader in providing computational software for all aspects of intelligent system design. Your role will be to bring machine learning/AI expertise to a cross-disciplinary R&D team working on the emerging boundary of scientific computing and machine learning/AI. The candidate should have a PhD in computer science / applied mathematics / computational physics / electrical engineering and the following preferred skills:
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Demonstrated expertise in theory and practice of neural networks / deep learning with working knowledge of contemporary topics (graph neural networks, attention mechanisms, transformer networks, reinforcement and transfer learning, etc.)
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Facility with classical methods of statistical inference
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Demonstrated ability to reduce algorithms and theoretical knowledge to practice and produce innovative research results
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Demonstrated programming proficiency in Python/C++.
- Familiarity with machine learning frameworks such as PyTorch, TensorFlow, Julia.
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Strong computer science background is a plus.
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Familiarity with recent research trends in physics-informed machine learning e.g. physics-informed neural networks, neural operator theory, DeepONets is a plus.
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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.
