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Field
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. Desirable criteria Experience working with generative models or large language models Experience with GPU-based model training or cloud computing Knowledge of synthetic biology or regulatory sequence design
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, and progression outcomes) and high-end compute (hundreds of NVIDIA H100 GPUs) via Mila and the Digital Research Alliance of Canada, and involves active collaborations with Stanford, Oxford, Google
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RTDS. Experience with software development. Experience with use of GPUs, multi-core CPUs, advanced computing (e.g., QPUs). Excellent written and oral communication skills. Motivated self-starter with
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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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and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be considered an asset Proven record in publication
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finite elements) as well as alternative discretization methods (e.g., Lattice Boltzmann Methods), and high-performance computing. A selection of possible research areas can be found on our website: https
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including code design, documentation and testing. Familiarity with optimization methods including Machine Learning (ML) techniques. Any experience with computations on GPUs. Working knowledge of Linux command
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that address real-world challenges and deliver positive business outcomes. The Institute for Insight is equipped with a computer cluster that includes multiple GPUs, designed for big data analytics for both
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(e.g. systems biology), or ordinary/stochastic differential equations. Experience in computational, statistical, or machine learning method development in any discipline. Experience in GPU computing
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computational resources and you will be able to carry out much of your routine work on our two new dedicated server machines (each 64 CPU cores, NVIDIA Tesla A30 GPU unit, 384GB RAM and over 24TB storage