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Field
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) Type of Contract Permanent Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms Publish and present your results in peer-reviewed journals and at
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computational experience, preferably on HPC systems with knowledge of parallelisation techniques and GPU programming. You will be expected to plan your own research, with guidance if required, and to assist in
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Shifting bits: Adaptive numerical precision for GPU software in particle physics and beyond (S3.5-COM-Richmond) School of Computer Science PhD Research Project Competition Funded Students Worldwide
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electromagnetic engineering problems. Programming experience in C/C++ and GPU is desired. Other qualifications being expected are as follows. Good analytical and problem-solving skills; Good analytical and
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with the domain of optical material behavior acquisition at a decent pace. What you bring to the table Very good C++ programming skills GPU & Shader programming, ideally knowledge of PBR (Physically
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., Kubernetes), public cloud hybrid infrastructure, and administering AI/ML hardware (e.g., GPUs) is highly desirable. Demonstrated ability to program in administrative scripting languages (e.g., Python, shell
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Domain-specific accelerators, e.g., GPUs, tensor cores, dynamic programming instructions (DPX) Software accelerators, e.g., DPDK, eBPF System security and Software Bill of Materials (SBOM) AI/ML
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, continuous integration and continuous delivery, software design, programming model, etc.). Conduct Annual Performance Reviews for direct report RSE staff. Supervise professional development of direct reports
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, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300 H100s). Ideal candidates will have a strong interest and proven