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characterization. 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
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kinetic monte 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
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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
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density and critical 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
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theory, quantum many-body theory, thermodynamics, statistical mechanics, or non-equilibrium physics Preferred Qualifications: Rich experience with transport measurements and characterization Basic
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potentials, and statistical mechanical theory. Collaborations with internal and external fusion efforts will be strongly encouraged. The successful candidate will be mentored and teamed with staff
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systems through data-driven modeling and optimization. The successful candidate will work at the intersection of thermal-fluid sciences, control theory, and artificial intelligence/machine learning to
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, algorithm design, probability theory, privacy definitions, and apply it to develop efficient privacy preserved federated learning model. Communicate and coordinate experimental results with other
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with specialists in synthesis, separations chemistry, theory, spectroscopy, and materials characterization. You will be responsible for executing the project generating publications, presentations, and
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-squares solvers, or scalable tensor kernels. GPU performance engineering — CUDA/HIP kernel design, communication–computation trade-offs, or performance modeling on heterogeneous systems. Theory of