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Field
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Python and experience with GPU processing of large-scale datasets. Excellent written and oral communication skills in English. We also value applications from people with the following experience
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or OpenMP. Experience in heterogeneous programming (i.e., GPU programming) and/or developing, debugging, and profiling massively parallel codes. Experience with using high performance computing for lattice
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scientific programming in Python and experience with GPU processing of large-scale datasets. Experience with inverse problems and 3D reconstruction methods for tomography, laminography, or a closely related
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Python and experience with GPU processing of large-scale datasets. Excellent written and oral communication skills in English. We also value applications from people with the following experience
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and hundreds million) Skills in AI-enabled and GPU-based calculations are welcome Good communication skills and the ability to work in a team environment Ability to work independently to solve critical
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trajectory optimization, nonlinear programming, or genetic algorithms. High-performance computing (HPC), parallel numerical workflows, or GPU-accelerated model execution. Terms of Appointment This is a full
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, microfluidics, laboratory automation, and GPU computing infrastructure. The opportunity to develop AI methods and scientific software that are directly deployed on cutting-edge experimental platforms. Vacation
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resources and a state-of-the-art in-house lab equipped with NVIDIA H100 and A100 GPUs and ten NVIDIA RTX Pro 6000 GPUs. Qualifications Requirements A doctoral degree or an equivalent foreign degree
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scientific domains. Preferred Experience: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling
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at the realization of large-scale diffusion language models and flow models. Specifically, tasks include but are not limited to: training using parallel GPUs, improving diffusion language models and flow models