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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods
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systems, ideally in both methods and applications. Experience in biomolecular simulations particularly nucleic acid simulations is desirable. An ideal candidate should have a solid background in physics and
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-efficiency to realized efficient Embodied/Edge-AI implementations. A key focus will be on investigating novel methods with strong theoretical foundations as well as their full-system deployment in real-world
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scientific research uses computer vision to pioneer new methods for 3D cellular structure analysis and cryo-electron tomography (cryo-ET). Dr. Qirong Ho – Assistant Professor of Machine Learning and Computer
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems Requirements: PhD in Mathematics, Computer Science
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custom experimental systems. These instruments may combine optical components, laser and spectroscopic methods, spin-control or magnetic-resonance techniques, electronics, data-acquisition hardware, and
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on investigating novel methods with strong theoretical foundations as well as their full-system deployment in real-world applications from different fields (like autonomous systems, healthcare, and robotics) with
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results of the state-of-the-art (SOTA) methods; Design and develop experimental facilities to allow implementing experiments on real platforms operating indoors and outdoors, to compare our proposed work
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must possess substantial experience in artificial intelligence and machine learning methods, specifically in AI-driven materials discovery, machine learning applications for materials, or generative AI
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Experience in machine-learning modeling for solid mechanics applications Experience in the development and coupling of numerical methods for solid mechanics modeling Experience in digital rock technology