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Field
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Develop scalable single-cell genomics and massively parallel screening pipelines for plant synbio parts discovery, construct evaluation, and plant characterisation Full-time appointment on a fixed
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substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
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for social phenomena on massive parallel HPC systems and their applications together with applications of artificial intelligence under the guidance of TP Dr. Nobuyasu Ito. The main target is to develop models
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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software for multi-arch environments Development in high-performance computing (HPC) or distributed systems Strong understanding of Linux toolchains, build systems (CMake), and debugging tools Parallel
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, Mixture-of-Experts; distributed training/inference (e.g. FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation pipelines for reasoning and agents. Federated & Collaborative
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Lab researches on a variety of computer systems topics including HPC resilience, data center power management, large-scale job scheduling and performance tuning, parallel storage systems and scientific
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and processes) by shadowing as well as receiving coaching and tutoring from one of our Senior PA Engineers. You will perform this task in parallel to specific Mission Classification activities described