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of computer graphics fundamentals, numerical methods, and GPU/parallel computing concepts. Experience with at least one major deep learning framework (PyTorch preferred). Excellent problem-solving skills and
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parallel and distributed systems, including performance tuning Programming and tooling such as C/C++, Python, CUDA, OpenMP, and Spack Linux-based systems, scripting, Slurm, and general systems engineering
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be investigated. Practical work involves collecting of plant material, wet lab work, bioinformatics, population genetic analysis, and growing plants to assess mutation accumulation. Your profile We
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Network with 15 funded 3-year PhD positions in parallel. Your profile Master Degree in environmental/natural sciences or engineering, or similar. Experience with developing computational models Preferably
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applications. HPC and orchestration of scientific data processing workflows. Parallel computing (GPU & CPU). good software engineering practices for scientific software (version control, testing, continuous
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using emerging non-invasive exhalomics (exhaled breath metabolomics) approach. The research program integrates animal experiments with advanced analytical techniques and bioinformatics to enhance
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targets an order-of-magnitude improvement in efficiency through parallelization, near-sensor processing, and heterogeneous architectures with specialized accelerators. chevron_right Working, teaching and
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, (Bio-)Engineering, Biological Sciences, Microbiology, Biochemistry, Systems Biology, Molecular Biology, Bioinformatics/Computational Biology, or related fields. Experience in designing and running
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, Biological Sciences, Microbiology, Biochemistry, Systems Biology, Molecular Biology, Bioinformatics/Computational Biology, or related fields. Hands-on experience with mass spectrometry. Experience with
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, we collect data through biological monitoring, environmental DNA methods, remote sensing, and field sampling, and use these data to answer questions with statistical and process-based models