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über solide Programmierkenntnisse, insbesondere in Python, sowie über Erfahrung oder ein ausgeprägtes Interesse an Robotik, Embedded Systems, IoT oder Softwareentwicklung. Hervorragende
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learning, Graph Neural Networks (GNN)/Grid Foundation Models and/or probabilistic methods and Monte Carlo simulation; - Programming knowledge in Python; Minimum requirements: Previous academic background in
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tools such as Python, R, MATLAB, Origin, Unscrambler, SIMCA or equivalent software. • Knowledge of analytical chemistry applied to food, environmental or biological matrices, with particular emphasis on
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data leakage, identify potential biases, and enhance model explainability. programming experience in Python, including PyTorch, OpenCV, Scikit-learn, Pandas, Numpy, Matplotlib, HuggingFace and MLflow
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analysis, and multi-modal integration is a strong asset (e.g, connectivity analysis). A proven track-record in current flow modeling (SIMNIBS) for TMS and/or tDCS. Skilled in programming (Python, MATLAB, R
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processing Very good programming skills (C, Matlab/Python, TensorFlow/PyTorch) and a passion for both theoretical and practical work are a premise The ability to develop own lines of thought, to
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, environmental sciences, or a closely related field Strong hydrological process knowledge Experience in statistical and/or data-driven modeling Strong programming skills (ideally in Python and/or R
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any other relevant subject. Specific skillsets include competence on programming languages such as Python/Matlab/C++, and knowledge-based skills on Electrical and Mechanical Systems. Priority would be
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management, wikis, advanced Microsoft office skills Technical programming skills (e.g. Matlab, Python, Julia, GAMS) are advantageous but not essential An interest in and understanding of energy transition
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skills are an advantage Solid R and/or Python skills, ideally paired with modern AI-assisted coding workflows (e.g., Claude Code), are a clear advantage Track record of first-author publications Excellent