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, especially for GPUs across multiple hardware vendors, as well as experience in software sustainability and design patterns is expected. Major Duties/Responsibilities: Collaborate within a multi-disciplinary
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align with one or more of these directions and who are excited about advancing the interface between AI and the natural sciences. Where to apply E-mail roland.aydin@uni-saarland.de Requirements Research
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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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, 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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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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, 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
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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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-day reliability and long-term evolution of clustered compute, GPU resources, high-performance storage, and the scheduler/tooling that support research at scale. Drives a culture of operational
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analytics techniques Familiarity with C++ and GPU programming Familiarity with Python programming Experience with modern software development practices to ensure code quality Experience with computational