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BTH's largest department, with just over 70 employees. The department conducts research in computer science, covering the subfields of big data and AI, parallel computer systems, visual and interactive
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Associate to join the Parallel Systems Lab (https://psl-ntu.github.io ) at NTU’s CCDS. The role focuses on designing, developing, and evaluating efficient, scalable systems for modern multi-agent HPC-AI
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is BTH's largest department, with just over 70 employees. The department conducts research in computer science, covering the subfields of big data and AI, parallel computer systems, visual and
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architecture of entirely new foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across
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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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international partners, therefore the candidate is expected to be multitasking as projects will progress in parallel. Active participation in other ongoing projects in the lab and clear communication of research
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to handle advanced level support. • Maintain high customer service standards in dealing with and responding to user issues and questions. • Installation, porting and tuning of parallel applications, tools and
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be the head of the computing unit. About the project The primary research goal will be to create quantitative performance models that describe a range of numerical methods, parallel programming models
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the computing unit. About the project The primary research goal will be to create quantitative performance models that describe a range of numerical methods, parallel programming models, and hardware platforms
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optimization, polynomial optimization, stochastic optimization, semidefinite programming, non-smooth optimization, and related fields. · High-Performance Scientific Computing: parallel computing (MPI, OpenMP