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learning algorithms on graphs to model, characterize, predict, and design the thermal and physical behaviors of diverse material systems. Responsibilities also include the development of software codes
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will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
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. This role will involve the development of novel lattice QCD algorithms and high-performance computing (HPC) codes, and/or exploring applications of artificial intelligence (AI) to lattice simulations
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motivated Post-Doctoral Associate to join our team with a strong background in robot control, machine learning, and differential geometry to work on the development of advanced algorithms to enhance
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developing new algorithmic approaches for TAPS data, interpreting the results in the context of phenotypic observations, and communicating these findings clearly to the broader team. You will prepare the
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in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several
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, calibration, and the development of analysis tools and software. Our key focus areas are the physics of jets, top quarks, and EWSB, including the development of novel machine-learning methods for high-energy
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is developing cutting-edge research on all aspects of computational imaging, from theory and algorithms, to applications in astronomy and medicine. Dr Wiaux is a Professor in the School of Engineering
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research in neuro-symbolic AI, with a focus on using generative AI and prompt engineering as a method to engineer knowledge graphs one can trust. This includes the design of algorithms and architectures, but
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through the Stanford Impact Labs Postdoctoral Fellowship Program (link is external) with Profs. Irene Lo, Itai Ashlagi, and the Stanford Impact Lab on Equitable Access to Education Postdoctoral Fellow