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, e.g., git, automated testing, packaging, documentation and code review. Experience with GPU computing or high-performance computing. Interest or experience in LLMs, tool-using agents, or agentic
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, and maintaining automated data pipelines for large-scale time-series or imaging datasets. Experience with HPC/cluster computing environments, including SLURM job scheduling and GPU-accelerated
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for recruitment positions and for general criteria for the position. Preferred selection criteria Knowledge of in Norwegian/Scandinavian language Experience with GPU based systems Experience with HPC based
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-on experience in one or more of the following technology areas: hardware/software co-design, performance optimization with heterogeneous and alternative computing systems (CPU/GPU/NPU/etc.), FPGA design, high
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resources and a state-of-the-art in-house lab equipped with NVIDIA H100 and A100 GPUs and ten NVIDIA RTX Pro 6000 GPUs. Qualifications Requirements A doctoral degree or an equivalent foreign degree
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funded doctoral position (TV-L E13) International research environment Joint affiliation with Saarland University and DFKI Access to state-of-the-art GPU and HPC infrastructure Collaboration opportunities
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with thousands of CPUs and GPUs, petabytes of storage, and high-speed, low-latency networks across multiple data centres in Switzerland. As research workloads continue to evolve, we are building a hybrid
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scientific domains. Preferred Experience: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling
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, or Transformer-based architectures Familiarity with training and evaluating models on GPU-accelerated hardware Ability to build reproducible experiments and maintain clear documentation Integrating AI/ML models
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evaluating them with clear performance criteria. Familiarity with real-time systems, embedded computing or GPU optimisation. Strong problem-solving and analytical skills. Fluent English, both written and