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Requisition Id 16540 Overview: We are seeking a Postdoctoral Research Associate who will develop and apply computational methods based on electronic structure theory and artificial intelligence
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. You will perform cutting-edge research on theory and modeling of dynamics in condensed matters. Major Duties/Responsibilities: Development of theoretical framework for driven and open quantum systems
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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
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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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computational foundations of that capability and help bridge the gap between Bayes theory and practical application: knowledge integration, developing robust likelihood frameworks, sampler behavior for long
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chemistry, radiochemistry, theory, spectroscopy, and materials characterization. This project will develop materials and processing technologies that will result in a transformational improvement in
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in condensed matter physics, or a closely related field completed within the last 5 years Extensive experience in neutron and/or X-ray scattering Experience in scattering theory, quantum many-body
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in Physics, Applied Mathematics, Computer Science, Electrical Engineering, or a related discipline. Familiarity with quantum computing and/or digital quantum simulation theory and practice. Familiarity
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hardware. As part of our team, you will perform research to develop new scalable quantum simulation algorithms, based on multi-linear representation theory, and apply them to real world applications spanning
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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