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Field
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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foundations. Candidates should possess an exceptional academic record and a strong mathematical background. Experience conducting large-scale computational experiments (e.g., multi-GPU systems) is advantageous
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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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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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optimization – with rigorous theoretical analysis. The ideal candidate has strong machine learning and AI expertise and is comfortable with – or eager to learn – large-scale multi-GPU experimentation
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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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segmentation." CVPR. 2022. [3] van Spengler, Max, and Pascal Mettes. "Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space." ICML. 2025. [4] Pal, Avik, Max van Spengler, Guido Maria D'Amely di
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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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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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on air-based cooling systems, they increasingly reach their thermal limits due to rapidly rising power densities in modern CPUs and GPUs. Liquid cooling technologies, such as Direct-to-Chip (D2C) can