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, uncertainty modeling, or decision-making under constraints. Experience with Python and modern ML frameworks such as PyTorch or TensorFlow. Curiosity for interdisciplinary research; prior experience with
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degree in computer science, mechatronics, or electrical engineering. Strong programming skills (C/C++, Python; hardware description languages such as HLS or VHDL are an advantage). Knowledge of computer
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problems Proficiency in data analysis and programming using at least one statistical program such as R, Python, or similar programming languages Experience with GAMS, GTAP, and Exiobase is an asset. Skills
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chemistry, or Applied Mathematics or Information Technology. Experience with numerical programming, machine learning or data analysis, preferably with Python. Solid background in technical mathematics
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of scientific programming (e.g., Python or R) Experience in handling large datasets is an advantage. Interest in natural language processing, text mining, and machine learning. Interest in the societal
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, advanced programming in Python) in small classes of max. 10 participants. Lecture series: QMB students suggest, invite, and host external speakers at this event. The lectures on QMB-relevant topics
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the analysis and simulation of analog, digital, or mixed-signal circuits, including SPICE and related tools (LTspice, Cadence, MATLAB, Python) Excellent communication skills and ability to work in a team are
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(beyond model training) Solid programming skills (Python required; C++/CUDA a plus depending on simulations) Interest in physics-based simulation, numerical methods, or computational engineering Motivation
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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