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Science and Quantum Computation Tensor Learning Team , Generic Technology Research Group, RIKEN Center for Advanced Intelligence Project (Team Leader: Qibin Zhao) Functional Analytic Learning Team
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learning and tensor libraries such as PyTorch We Are Delivering Scientific Excellence Los Alamos National Laboratory is more than a place to work. It is a catalyst for discovery, innovation and achievement
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-order differentials. Differentiable wind models for accurate re-entry simulations. High-order State Transition Tensors usages and efficient computation. Manoeuvre detection and estimation of non
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functional data analysis, tensor regression, high-dimensional variable selection, longitudinal and survival analysis, machine/deep learning, bioinformatics methods in -omics data are preferred. Demonstrated
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Mathematical Physics in the last 40 years. Researchers in this area will be expected to have knowledge of the aspects of Quantum Groups associated to Representation Theory, Tensor Categories, Poisson Geometry
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or novel applications of machine learning. Expertise in deep learning techniques such as transformers, LLM, GNN, generative models OR advance matrix method such as matrix/tensor completion, non-negative
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with tensor networks, AI in physics and materials, etc.). A Ph.D. in Physics or a closely related field is required. The initial appointment will be for one year, and is renewable for another one or two
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to tensor network states (e.g. DMRG, MCTDH, TTNS) and applying them to problems in theoretical quantum chemistry and theoretical chemical physics. Of particular interest are candidates who are interested in
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Ph.D. in Physics or a closely related field is required. Candidates with a strong background in quantum many-body theory and experiences in quantum Monte Carlo and tensor network methods are encouraged