At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

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:

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.)Facility with classical methods of statistical inferenceDemonstrated ability to reduce algorithms and theoretical knowledge to practice and produce innovative research resultsDemonstrated 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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