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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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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 advance
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experience through research, internships, and industry-sponsored capstone projects. With a versatile curriculum spanning software, systems design, nanofabrication, and machine learning, the program prepares
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, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification. Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI
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generating fusion energy. This research will focus on the chemical speciation and transport of tritium in the molten salt blankets using ab initio quantum simulations, machine learning potentials, and
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and talented post-doc candidates to work on Bioinformatics and Computational Biology in Cancer Genomics and Immunology. This position will be involved in the development and/or application
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position of which they have applied. For general application assistance or if you have questions about a job posting, please contact Human Resources at 479.575.5351. Department: Post Docs, GAs and Hourly
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analysis. ● Prior work on kinase or other signaling-protein conformational dynamics, phosphorylation-driven activation, or allosteric regulation. ● Familiarity with machine learning and deep learning
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Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
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modeling, molecular interactions/energetics, and tools such as AlphaFold, Rosetta, or MD simulations. Solid programming skills (Python); familiarity with machine learning is a plus. For both profiles, we