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Field
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, implementing input sanitization, and contributing to AI‑safety research. Utilizing GPU/TPU resources, mixed‑precision training, and distributed training frameworks such as DeepSpeed or ZeRO. Prior work
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members in designing and integrating solutions into the AI(X) compute, software and data infrastructure stack, hardening these solutions, testing these on modern high-performance GPU compute clusters, and
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benchmark them with a realistic case study. The main focus of the project can develop either more in the mathematical theory of MCMC, the implementation of code for the Jülich supercomputers (GPU/CPU
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include: Assist in porting quantum computing optimization codes to CUDA-Q for use with NVIDIA GPUs. Collaborate with NLR researchers on developing formulations and algorithms for solving optimization under
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following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high-performance computing (HPC
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and have prior experience with remote GPU and HPC services. After the qualification requirements, great emphasis will be placed on personal skills. Target degree: Doctoral degree Information regarding
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Infrastructure - Collaborate with research computing teams and HPC centers to architect AI infrastructure solutions that support both administrative and research computing needs, including GPU-accelerated
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megawatts. To transfer energy efficiently from the grid to CPUs/GPUs, higher system voltages are required in data centres/computer racks, and efficient power electronics converter systems based on SSTs
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good understanding of the physics of scattering and antenna radiation Programming experience in C/C++ is necessary while experience in parallel and GPU computing is most desired More Information Location
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and equipment required for this project, including a confocal optical tweezer system housed in the Henry Royce Institute, an ever-expanding biophysics suite in the School of Bioscience, multi-user GPU