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and training in GPU programming, data science applications, and scientific computing workflows for the SciNet advanced computing consortium. The incumbent supports users in designing, training, and
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realizations of the proposed latent reasoning algorithms. Quantitative comparisons with different hardware backends. Design conventional (GPU-based) deep neural networks for comparison. Publish research
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networks, as well as courses in Python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and
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, and audio research, including Aalto University’s large-scale scientific computing cluster with CPU and GPU nodes, access to CSC’s national computing infrastructure including LUMI, and the Aalto
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foundational computational literacy—including programming, data handling, quantitative methods, and confident research use of Python, R, Git, and Jupyter—is a baseline for the appointee, not the position’s
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appointment. Strong foundational computational literacy—including programming, data handling, quantitative methods, and confident research use of Python, R, Git, and Jupyter—is a baseline for the
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and graduate research assistants, and presenting at design reviews and project milestones. Conducts benchmarking and performance evaluation of LLM serving and agentic workflows on high-performance GPU
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mathematical problem-solving skills Excellent programming skills (Python and modern machine learning frameworks like PyTorch, JaX, etc.) and experience in working with HPC/GPU environments A track record
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trustworthy software and systems, spanning foundations, programming languages, tools and practical methods that help shape dependable digital infrastructures. This project is a collaboration between the
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<span lang="en">Universitätsklinikum Hamburg-Eppendorf</span> | Hamburg, Hamburg | Germany | 6 days ago
resources for travel and conferences
- Work on cutting-edge AI methods with direct clinical relevance
- Access to large-scale multimodal biomedical datasets and modern GPU infrastructure