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quantitative data analysis using R and/or Python; experience with statistical modelling and the analysis of complex biological data; a strong interest in exposomics, multi-omics integration, and brain–body
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or analytical skills, or a clear motivation to develop them further. You have experience in neuroimaging and brain network analysis. You have programming or data-analysis experience (e.g. FSL, Python, MATLAB, R
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that connects engineering and clinical application. You have programming experience in Python, MATLAB, or similar languages. You are motivated, collaborative, and communicative. Additional Information Benefits
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skills (Python) and knowledge of deep-learning frameworks (PyTorch) are expected. A certain affinity towards turning complex concepts into real-world practice is desired. The successful candidate is
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Aerospace Engineering, Aeronautics or a comparable degree, thorough knowledge of AI/ML methods, acoustics, and air traffic management are preferred, as well as excellent programming (Python, Java, C++, …) and
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and transport in porous media; Strong analytical skills and ability to integrate experimental observations with computational approaches; Proficiency in at least one programming language (Python, MATLAB
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quantitative methodological skills, for instance knowledge of (spatial) data analysis, hydrological modelling, AI-based methods Experience with programming languages (e.g. Python) Motivation to cooperate in a
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interest in medical imaging, radiation safety, and AI. - Experience with Python, deep learning frameworks (PyTorch/TensorFlow), or image analysis (preferred). - Good communication skills and fluency in
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in Python and affinity with large geospatial datasets. Interest in interdisciplinary research at the interface of geoscience, engineering, and societal impact. Good communication skills and willingness
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for hands-on experimental characterization techniques and data analysis. Skills in programming (e.g., Python, MATLAB) and simulation tools. Expertise in photonic integration is not a must, but having relevant