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Field
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for the position. Preferred selection criteria Experience with machine learning and neural networks Basic knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux
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be advantageous: Experience with scientific expeditions Experience in computer-aided analysis of biological sequence datasets (e.g., with R, Python and Bash/Linux environments) Basic understanding
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(meta-)genomes Experience in the computer-assisted analysis of large biological datasets (e.g., using R, Python, and Bash/Linux environments) Very good written and spoken English skills Ability to work
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experimental data. • Proficiency in scientific programming and data analysis tools (e.g., Python, R, Linux/Unix environments). • Demonstrated track record of publishing scientific results in peer-reviewed
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Research Center Borstel - Leibniz Lung Center | Hamburg Gro Borstel, Hamburg | Germany | 2 months ago
of the Linux command line • Familiarity with Linux-based computing environments; experience with high-performance computing (HPC) is an advantage • Experience with workflow management systems such as Snakemake
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programming using Python, C/C++, ROOT or equivalent tools. • Experience in Linux environments and scientific computing. • Experience in the development and operation of complex experimental or technological
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programming experience - Strong experience with UNIX/Linux - Familiarity with current hardware and software vulnerabilities and mitigations - Experience with RF and SDR technologies Requirements: BS degree in
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programming experience - Strong experience with UNIX/Linux - Familiarity with current hardware and software vulnerabilities and mitigations - Experience with RF and SDR technologies Requirements: BS degree in
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Linux and high-performance computing environments Rosetta or related protein design software Our offer A fully funded PhD position with an attractive salary. A dynamic, highly stimulating, and
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control with Git, typesetting with LaTeX, use of Linux computers; Experience with convolution and transformer-based neural networks for image analysis; Experience with graph-based methods, and graph