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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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, 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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, preferably in a higher education, research, or HPC environment. Demonstrated experience with Linux system administration at scale, including cluster computing environments. Experience supporting HPC or GPU
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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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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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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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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
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Linux/Unix, and running algorithms on CPUs and GPUs Deep learning frameworks, particularly Python and PyTorch Medical image segmentation and computer vision model development Large language models and
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, virtualisation technologies and performance optimisation. Hardware acceleration technologies and modern compute architectures, including GPUs and high-performance networking technologies. Programming and scripting