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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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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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programming distributed systems; Experience with parallel and distributed File Systems (e.g., Lustre, GPFS, Ceph) development. Advanced experience with high-performance computing and/or large-scale data centers