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Field
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to deliver responsive, reliable and future-ready IT solutions. Key Responsibilities: • Manage and maintain the centre’s IT infrastructure, including servers, networks, and GPU systems. • Plan and
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phlebotomy. Additionally, the GPU is home to the GI Division's Motility program offering short and long motility studies, Bravo, Impedance Probes and EndoFlip diagnostic tests. The GPU is also home to
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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Sheffield to enable massively parallel processing of ABMs on NVIDIA graphics processing units (GPUs), without the need for specialist understanding of GPU programming or optimisation. This project will
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strategies for programming, modeling, and integrating reconfigurable/spatial architectures, such as FPGAs and ML accelerators, within heterogeneous ICT ecosystems.Reconfigurable and Spatial hardware, such as
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programming proficiency in Python and C/C++. Expertise in ensemble learning (e.g., Random Forests, Gradient Boosting, bagging/stacking frameworks). Hands-on experience with parallel or GPU-based computing (CUDA
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++ skills. GPU programming experience (CUDA, HIP, SYCL) is beneficial. Degree MSc student in computer science or computational science and engineering. Work location Zürich Topic C: CHatterbox: A RAG-Powered
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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/C++, and Python. Working knowledge for parallel programming models (MPI, OpenMP, CUDA, SYCL) Proven track record in GPU driver development, performance profiling, and debugging. Familiarity with Linux