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Field
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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aims to develop a new generation of mobile robots capable of withstanding shocks, absorbing impacts, and recovering from collisions in complex, unstructured settings. The concept of resilience here
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learning algorithms in PyTorch. Expertise in object-oriented programming, and scripting languages. Parallel algorithm and software development using the message-passing interface (MPI), particularly as
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data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms. Assist with organizing large-scale multimodal neuroimaging dataset, brain
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scientific data Major Duties/Responsibilities: Design and implement advanced AI architectures and workflows for imaging and spatiotemporal data. Develop efficient and scalable training algorithms
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principle, active inference, triple equivalence, and related theories 2.Develop neuromorphic (biomimetic) learning algorithms for next-generation artificial intelligence 3.Develop a universal generative model
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration
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connect to our group’s work and how this position supports their career development goals. Possible research topics include (but are not limited to): Optimization algorithms for machine learning (stochastic
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) for the high-luminosity phase of the LHC, in particular on its mechanical design, on the generation of the L1 trigger primitives, and on the development of offline reconstruction algorithms. In addition, it is
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. Essential Duties and Responsibilities Neuroimaging data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms. Assist with organizing large