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Field
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computing environment that includes GPU clusters, large-memory servers, and an NVIDIA DGX B200 system. These resources support the training of large multimodal models involving audio, video, language
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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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variety of computational devices (e.g. CPUs and GPUs) while ensuring overall consistency and performance. - contribute to identify new CSE applications domains, such as condensed matter systems, quantum
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, forward-looking, and varied research fields and projects, with numerous development opportunities Modern hardware and infrastructure at the workplace, from compute and GPU servers to supercomputers
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with OFDM modulation required. Skills Programming skills in MATLAB and or Python required, experience with wireless testbeds desirable, some familiarity with GPU programming desirable (to support
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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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programming (Shared and Distributed memory, GPU programming etc.) Demonstrated experience with distributed memory MPI programming Experience with collaborative software design, development, and testing
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/TimeSformer, CLIP/BLIP or similar) in PyTorch, including scalable training on GPUs and reproducible experimentation. Demonstrated experience building explainable models (e.g., concept bottlenecks, prototype
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scientists and engineers are accustomed to. Moreover, the vast majority of the performance associated with these reduced precision formats resides on special hardware units such as tensor cores on NVIDIA GPUs
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disease insights. The lab has state-of-the-art computing capabilities with an in-house cluster serving 80 CPU cores and 1.5TB of RAM, as well as a newly acquired NVIDIA DGX box with eight H100 GPUs and 224