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, OpenFOAM), and plasma physics (XGC, IPPL). Expected qualifications: A Master's degree in Computer Science or Applied Mathematics. Necessary knowledge: Modern C++, GPU computing with CUDA/SYCL, MPI, Krylov
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GPU-accelerated codes. A strong publication record in top-tier journals and conferences. Excellent communication skills and a desire to work collaboratively in a team-oriented research environment
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, workflow orchestration, and high-throughput data processing, as well as experience working with large biological datasets in GPU-based computing environments. What we provide: A competitive compensation
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pipelines for large-scale biological datasets. The Research Associate will: Design and implement high-performance workflows integrating GPU programming, deep learning, and large-scale data integration. Apply
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GPU clusters and high-performance computing facilities to accelerate your research. Good transport links in a central location with a free job ticket as a contribution to climate protection Low-cost
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. Innovative visualization tools and highly automated analytical pipelines powered by GPU technology. Mentorship from experienced scientists in data analysis and management, with an expertise in delivering high
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Generative AI (GenAI) applications, including GPU-hosted LLMs, containerized workloads, and internal agent platforms. Manage Model Context Protocol (MCP) servers, ensuring context routing, memory persistence
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will have access to extensive computing resources, including access to ~100 Nvidia H200 GPUs. Applicants should hold a Ph.D. in computer science-related or biomedical research fields relevant
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: Experience in real-time simulation hardware like Opal-RT and RTDS. Experience with software development. Experience with use of DSPs, FPGAs, advanced computing (e.g., GPUs, QPUs). Excellent written and oral
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on hardware which may include CPUs, GPUs and FPGAs. In particular we are interested in applicants who have experience of modern coding practices and software techniques. The post-holder will work with Prof. Ben