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-body quantum geometry; altermagnetism; cavity quantum science; quantum non-equilibrium processes; Casimir physics , Non-equilibrium quantum physics , Physics-informed machine learning , Quantum chaos
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tools for the design, analysis and evaluation of socially intelligent systems that aim to collaborate with humans in learning and decision-making tasks, often with the aim of improving health. Visit https
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astrophysics (completed by the start date), demonstrated experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
management and planning skills; strong problem-solving and organizational skills; strong computer experience with Microsoft Office suite; and experience with or willingness to learn new laboratory management
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close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods. Key responsibilities Develop effective
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structure and quantum chromodynamics, 2) Experience in the use of machine learning and high-performance numerical computations, 3) Readiness to teach courses in physics and computer science in English
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perform experiments using an array of state-of-the-art techniques from systems neuroscience, genetics, and physiology. More information about this lab can be found on his website https://knightlab.ucsf.edu
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, doctoral students and postdocs and encourages them to pursue international recognition; aims to acquire competitive research funding from national and/or international funds and submits effective research
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knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required. Record of peer-reviewed publications. Knowledge in one or more of the following areas is
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learning, computational biology, and AI for science The postdoc will work at the interface of machine learning, genomics, and scientific computing, contributing both methodological innovation and