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Field
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, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300 H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding, or engineering
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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turbulence. Experience with GPU programming, FPGA, and DNN in image recognition is a great plus. Track record of publications and conference presentations. Experience with hands on lab work. FLSA Exempt Full
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. First, efficient and scalable training procedure are still needed, irrespective of whether the training is done off-line on a traditional GPU-based architecture, on neuromorphic hardware. Second
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computational infrastructure such as A100 and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research
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, with PyTorch and/or other GPU programming tools is also necessary. You should have completed all requirements for your PhD by the time you are hired. How to Apply: Candidates who have most, but not all
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, Matlab Preferred Qualifications: Experience in thermos-fluids in porous media. Experience in High-Performance Computing (HPC) on CPU or GPU platforms. Experience in mentoring of graduate and undergraduate
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FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
environment Access to state-of-the-art tools and computational infrastructure, including CPU/GPU clusters Opportunity to contribute to cutting-edge research in plant evolution and genomics Support
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part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300
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provide a performance or efficiency advantage, and determine scenarios where conventional AI accelerators (such as embedded GPUs or FPGA-based accelerators) remain more appropriate due to data