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will perform enhanced sampling molecular dynamics simulations using neural network potentials and deploy statistical mechanics (e.g., rare-event theory), Markov-state models for molecular kinetics
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codes. Collaborating with domain scientists to apply sparse algorithms to DOE mission applications and mentoring and working with undergraduate and graduate students. Basic Qualifications: A PhD in
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in synthesis, separations chemistry, theory, spectroscopy, and materials characterization. You will be responsible for executing the project generating publications, presentations, and invention
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statistical mechanical theory. Collaborations with internal and external fusion efforts will be strongly encouraged. The successful candidate will be mentored and teamed with staff in the National Center
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approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design, probability theory
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temperatures based on these predictions. The position resides in the Nanomaterials Theory Institute (NTI) within the Theory and Computation Section (TACS) at the Center for Nanophase Materials Sciences (CNMS
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carlo), as well as experience in developing and/or applying advanced AI/ML methods to accelerate materials discovery. The project will involve integrating such theory-informed AI-models for creating
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
-approaches that allow integration of different theory, simulation, and experimental protocols. The research is designed to provide opportunities for development of your experience and scientific vision