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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
., using FEniCSx) Advanced knowledge of scientific programming, preferably in Python, including experience with implementing machine‑learning methods (e.g., PyTorch) Excellent spoken and written English, as
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PixHawk Autopilot, Arduino boards, Raspberry Pi - or equivalent Experience with ROS/ROS2 Experience with programming languages like Matlab, Python, C++ Familiarity with machine learning and/or deep learning
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Knowledge of statistical methods in the context of biological systems Experience with programming (Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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, Mechatronics, Data Science, or a closely related field Strong background in signal processing, measurement systems, or data analysis Programming skills in Python, MATLAB, or similar scientific computing
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, including, but not restricted to, geometry or shape optimization, parameter optimization, or multi-objective optimization. · Strong programming skills (e.g. Python, C/C++, R or similar) and experience
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biomedical research. Your profile Master's degree in computer science or related discipline Experience with Python and recent deep learning frameworks (e.g. Pytorch, MONAI) Strong interest in image analysis
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, computational biology, bioinformatics, data science, or related fields Strong interest in clinical and biomedical data, translational research, and health informatics Experience with programming skills in Python
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Strong background in differential equations and numerical methods Solid programming skills e.g. Python, C++, Julia or similar Interest in interdisciplinary research bridging mathematics and environmental
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++ and/or Python Experience with SOFA, FEniCSx or similar simulation frameworks is a strong plus Motivation to work at the crossroads of mechanics, AI and medical technology, in close collaboration with
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, or a closely related field Strong programming skills, e.g., Python, and familiarity with machine learning and/or software engineering workflows; experience with Git and empirical evaluation Experience