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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
ML concepts and architectures and hands-on experience with open-source AI/ML packages (such as pytorch, scikit-learn, tensorflow, JAX etc.). Preferred Qualifications: Good grasp of concepts in solid
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the development of AI architecture for holistic genomic photosynthesis modeling. Evaluate performances of AI genomic photosynthesis models. Report advances to program management and broader scientific communities
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software tools and frameworks Implementation of scalable numerical algorithms on HPC architectures Excellent written and verbal communication and interpersonal skills. The ability to obtain and maintain a
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elements methods Modern machine learning software tools and frameworks Implementation of scalable numerical algorithms on HPC architectures Excellent written and verbal communication and interpersonal skills
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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other
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, Scikit Learn, etc., in applied problem-solving contexts. Understanding of machine learning algorithms (gradient descent, random forests, etc.) and deep neural network architectures (Transformers). A broad
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for kinetic and/or fluid equations Multiscale problems and model reduction Modern machine learning software tools and frameworks Implementation of scalable numerical algorithms on HPC architectures Excellent
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synthetic data Experience with generative AI methods and libraries (architectures like large language models and vision transformers, inference engines like vLLM, domain specific languages like Triton