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. Experience in programming (in particular, Python) and an interest in machine learning, data analysis, or scientific computing are expected. Prior experience with machine learning or optimization methods is
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software as python packages Reporting findings and methods in conference and journal papers Your profile Masters, Diploma or equivalent degree in IT/computer science/statistics/applied mathematics/data
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, e.g., Python, C/C++, Fortran Willingness to work independently and think critically Excellent communication skills Ability to work well in a team and contribute to a diverse and inclusive workforce What
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well as experience in omics data analysis, and possesses solid English-language skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX
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, catalysis studies - in particular electrocatalysis and Python programming Experience within the following fields is highly beneficial: Transmission electron microscopy, Electrochemistry, Image analysis, Big
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electrical engineering, computational engineering, or a related discipline A strong foundation in power system modelling and simulation Solid programming skills (Python, C++, or comparable languages) Interest
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
, data acquisition systems, or scientific programming (e.g., Python, MATLAB) Ability to work in an interdisciplinary environment at the interface of physics, engineering, and biomedical research Excellent
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
programming skills (Python/R and/or Rust) and experience with reproducible workflows (Nextflow, Snakemake, WDL, Git, containers). Excellent communication skills and ability to work collaboratively in a
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learning pipeline in Python (using e.g. PyTorch) - validation of your results in collaboration with colleagues from various application areas (cross-disciplinary) - publication and presentation of your
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pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with colleagues from various application areas (cross-disciplinary) publication and presentation of your scientific results