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initiatives and process improvement. Experience provisioning and managing GPU-enabled infrastructure (NVIDIA GPUs, CUDA, multi-GPU systems) in cloud and/or on-prem environments. Familiarity with GPU
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dealing with the large number of plankton types and with unresolved ecological processes especially around mortality. Modern, GPU based, modelling infrastructures provide promise towards running models
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in chemistry, physics, or related field. At least 2 years of experience developing quantum Monte Carlo algorithms. Strong problem-solving and analytical skills. Python programming experience. GPU
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Kubernetes, and GPU infrastructure; (f) have a good command in both written and spoken English and Chinese; and (g) have good communication skills. Preference will be given to those with experience in
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Python and experience with GPU cluster environments (e.g., SLURM) are a plus. Special Instructions Please provide a CV, a Research Statement, and two or more letters of recommendation. The target start
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and multi-omics data environments Modern GPU, and high-performance computing resources, plus dedicated research-engineering support Close integration with clinicians and clinical trial/implementation
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expertise in optimisation; Computer Science, with expertise in design and analysis of algorithms and high-performance (GPU) computing; Industrial and Systems Engineering, with AI in process mapping and Conops
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the strategic direction for the unit while personally driving key programs forward. You will own the Center’s research accelerator portfolio - including GPU grants, university partnerships (e.g
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inclusive workforce Access to our GPU-accelerated HPC cluster and laboratories with cutting-edge sequencing technologies and molecular assays Performance-based remuneration and other benefits The opportunity
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Language Model (LLM) GPU cluster to ensure stable and reliable operation of training tasks; (b) handle GPU node failures, IB network anomalies, CUDA/NCCL errors and Kubernetes scheduling failures, perform