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members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience in sampling algorithms and related areas
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for applications. The ideal candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems
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the following areas is preferred, and an exceptionally strong candidate in a single area is also encouraged to apply. Relevant areas include: Parallel and distributed graph and or ML algorithms
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Professor of Machine Learning and Computer Science, MBZUAI. Dr. Ho specializes in distributed machine learning and systems architecture. His expertise drives the design of robust, parallelized systems
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
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development skills; distributed or parallel computing is a plus. Experience designing and executing field experiments in urban or environmental settings, with willingness to engage in fieldwork in dense urban
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National Lab, University of Tokyo etc.), the PhD candidate is expected to research on some of the following themes: New algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient
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distributed intelligence across the computing continuum. In this role, you will have the opportunity to lead and contribute to cutting-edge research aimed at transforming scientific data management and
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communication in parallel/distributed AI/ML Enhancement of AI/ML with in-network computing & processing Adaptation & optimization of AI/ML software libraries for non-conventional hardware architectures Physics
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of computer scientists. Extent: 100% employment, distributed as 80% research and 20% departmental duties (typically teaching at the BSc or MSc level). The position is meritorious for future roles in