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Python or other equivalent programming languages. Strong foundation in data analytics and data-driven modelling. Able to build and maintain strong working relationships with people within and external
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assembly, population genetics, statistical genetics, complex trait mapping, and high throughput sequencing genome and programming proficiency in R, Python, Perl, C/C++, Java, and SAS are highly desirable
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signal processing skills (e.g., using Python and/or MATLAB). Ability to work effectively in an interdisciplinary team and communicate clearly across disciplines. Experience handling large datasets and
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/multiphysics, wave propagation, computational mechanics). Evidence of scientific programming and good software/reproducibility practice (e.g., Python/MATLAB/C++ and/or established modelling platforms
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to diverse academic and industrial audiences. Proficiency in Python and deep learning frameworks such as PyTorch. Experience with Linux environments and GPU cluster management is essential. Competent in
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students and working effectively in a team-based research environment Proficiency in common computational chemistry software and coding techniques e.g. Gaussian/Amber/Schrödinger/python/bash For more
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or TensorFlow. Advanced programming and high-performance computing skills, including proficiency in Python and/or C/C++, experience with GPU acceleration, and the ability to develop, test, and maintain research
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and skill in programming with MATLAB or Python. Research experience in MEG, in vivo electrophysiology, in vivo two-photon/miniscope imaging, slice electrophysiology, and mouse brain surgery is desired
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master exam before 30.06.2026. It is a condition of employment that the master's degree has been awarded. Good programming skills (Python, Matlab or Julia) are a requirement. A solid background in
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physics, with experience in thin-film deposition, device fabrication, and electrical transport measurements Familiarity with the theory of magnetism Proficiency in MATLAB/Python and LabVIEW for data