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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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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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of vector query processing, serverless/just-in-time query execution, parallel and distributed data management, and data management systems on emerging hardware Passionate about inventing, building, and
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background in one or more of the following areas: computer architecture, high-performance computing, quantum computing systems, distributed and heterogeneous computing, runtime systems, or performance modeling
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and solid-state qubits, as a foundation for distributed quantum computing. You will help build and operate two experimental platforms, laser-cooled neutral atoms and rare-earth-ion-doped crystals, and
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theoretical foundations of distributed quantum computing . Our long-term objective is to understand which distributed tasks admit a quantum advantage, especially in the context of the LOCAL model of distributed
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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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and epigenetic manipulation of AML cells and massively parallel reporter assays to investigate gene regulation in leukemia. Computational analysis of these data will involve applying existing tools and
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. Design and implement distributed and parallel approaches that efficiently leverage large-scale computing resources, including heterogeneous CPU/GPU systems, along with the possibility of working with