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Field
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scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing predictive performance, generalisation
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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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Computer Science, a Bachelor of Science degree in Cybersecurity, a Master of Science degree in Computer Science, and two multidisciplinary PhD degrees in Computer Science and Information Systems and Engineering and
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-time Hours Per Week 37h15 Offer Starting Date 26 Oct 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a
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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 the group. Strong knowledge of performance modeling, simulation, and benchmarking of parallel and distributed computing systems and of the workflow systems that run on them. Familiarity with the FAIR data and
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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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functioning in a dynamic technological environment. Preferred Qualifications: Experience with parallel computing, GPU operation (CUDA Toolkit), multi-GPU training, and distributed frameworks for machine
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dependencies for execution on Alps / CSCS infrastructure. Run and monitor Slurm-based training and evaluation jobs. Debug failures related to distributed execution, checkpointing, filesystem performance