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 expertise in statistical inference to a cross-disciplinary R&D team working on the boundary of statistical analysis, electronic design automation, and machine learning / AI.
The candidate should have a PhD in computer science / applied mathematics or closely related field and the following preferred skills :
Demonstrated expertise in statistical inference : significance testing (p-value, confidence intervals), Monte Carlo methods (random sampling, density estimation), design of experiments, Bayesian statistics, variational Bayes.
Facility with classical methods of machine learning : regression (linear, logistic), regularization (ridge, lasso), classification (SVM, k-nearest neighbors), ensemble methods (decision trees, random forests, boosting, gradient boosting).
Familiarity with contemporary techniques in AI / deep learning is a plus (graph neural networks, transformer architectures, reinforcement and transfer learning, representation learning, etc.).
Demonstrated ability to reduce algorithms and theoretical knowledge to practice and produce innovative research results.
Demonstrated programming proficiency in Python / C++.
Strong computer science background 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.
Exposure to electronic circuit design automation problems is a plus.
Candidate should expect to work with a cross-functional engineering team in to perform cutting-edge research but ultimately deliver innovative technologies in a production environment.
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