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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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labs The project offers access to GPU-enabled HPC clusters, high-end workstations, and a collaborative, interdisciplinary environment. The student will develop a versatile skill set applicable to careers
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-tuning only a small set of low-rank matrices for each agent role, drastically reducing GPU memory and training time while preserving the model's pre-trained knowledge. The primary outcome of this research
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computing and the use of GPU clusters. Entry Requirements Acceptable first degree - Computer Science/Physics/Maths The standard minimum entry requirement is 2:1. First class in bachelor degree or a master
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) Programming skills in C/C++, Rust or other relevant low-level languages is desirable. Experience programming GPUs, FPGAs, ASICs or other specialised hardware is desirable Studentship and eligibility
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simulation workload and update the solver data structures when the mesh changes. These approaches would be applied on modern large-scale heterogeneous parallel computing environments where both CPUs and GPUs
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, mobile platforms, industrial sensors/cameras, GPU workstations, and cloud platforms. Training covers research methods, scientific writing, open-source best practices, and impact/engagement. You’ll be
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transformations at such interfaces, and how they are influenced by external electric fields and electrolyte composition. Access to high performance computing facilities including GPU clusters will be provided
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resources, including 200+ NVIDIA A100 GPUs and group workstations. Image quality will be assessed using quantitative metrics and clinical expert qualitative review. Privacy safeguards will be built