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Field
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research to systematically understand cancer biology, identify diagnostic and prognostic biomarkers, and improve cancer therapy. Projects will involve the development of AI solutions, including machine
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via autophagy and lysosomal targeting in learning and memory and disease models using rodents and iPSC-derived cell cultures. These mechanisms will be investigated in both healthy conditions and
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for learning, creating, discovering, and innovating, and are encouraged to learn from failure. Show Your Fire by joining our team and exhibiting your passion and pride in your work as part of our UNT
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of substitution models using large dataset, successful applicants must then have a PhD and demonstrated experience in discrete choice models, machine learning techniques, big data, and optimization
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QUALIFICATIONS Education: PhD or Doctorate degree Medicine (MD) required Experience: No previous experience required Knowledge, Skills and Abilities: Learning Agility: Ability to learn new procedures, technologies
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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journals and conferences. This role provides a unique opportunity to work with the world’s first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric
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to generate data which can be coupled with robust and physics-informed machine learning and reduced-order modelling methods for model predictive control and reinforcement learning. PhD (or soon to be completed