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] Subject Area: Statistics and Machine Learning Appl Deadline: 2025/12/01 11:59PM (posted 2025/09/04, listed until 2026/02/01) Position Description: Apply Position Description The Department of Mathematics
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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 1 hour ago
design. These unanticipated faults and disturbances present great challenges in the detection, isolation, and control of dynamic systems such as electrical power. Recent advances in machine learning
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, United States of America [map ] Subject Areas: Computer Science Machine Learning Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Appl Deadline: none (posted 2025/08
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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in machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to adel.bibi@eng.ox.ac.uk For more information about working at
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. The successful candidate will be joining the Quantum Optics Theory group led by Prof. Dr. Maciej Lewenstein. The successful candidate will work on Machine Learning research. Share this opening! Use the following
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Applied Mathematics, Computer Science, or Theoretical Physics (at the time of appointment). Background in machine learning theory or in one or more of: high-dimensional probability, random matrix theory
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in