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Field
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or large language models Experience with GPU-based model training or cloud computing Knowledge of synthetic biology or regulatory sequence design Previous collaboration with experimental biologists
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emphasis on programmability and the characterization of AI capabilities in CPUs, GPUs, and dedicated accelerators; Identification of computational patterns suited to AI-enhanced processors and standalone
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GPUs). Research Associate: Hold a PhD in high performance computing, computational fluid dynamics or a closely related discipline*, or equivalent research, industrial or commercial experience. Research
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Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
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for early detection, differential diagnosis, progression monitoring, and treatment design. Key attractions are access to a high-performance computing cluster, NUS HPC (H100/H200 GPU clusters), two 3T Prisma
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well as access to the group dedicated computing cluster environment with H100, L40s, and A40 GPUs. This post is funded by the UKRI Future Leaders Fellowship, a flexible long-term public funding scheme
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optimizing PIC algorithms for modern heterogeneous architectures, including CPUs, GPUs, and other accelerators, the project seeks to achieve unprecedented efficiency and resolution in plasma simulations
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conditions. Implementing a multimodal approach for large-scale data analysis using CPU and GPU Solutions at the UM6P Data Center. Innovate and improve image analysis algorithms for plant trait quantification
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library research and field explorations. Run experiments, analyze results, and prepare research outputs. Execute large-scale training jobs on GPU clusters, track metrics, visualize findings, and contribute
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of this role is to oversee the construction and management of a High-Performance Computing (HPC) cluster comprising 4,500 CPU/GPU cores, BeeGFS storage, and Infiniband interconnects. The ideal candidate will