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requirements Essentials: Basic knowledge of power systems and machine learning/AI. Completed an MSc degree in a highly technical related discipline (computer science, electrical engineering, etc.) and you were
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, Machine Learning, or Smart Energy Publication record in peer-reviewed journals or conferences, commensurate with stage of career Good programming skills in Python, R, Java, or Matlab Experience
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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements
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the optical-to-radio wavelength range, from major surveys and space telescopes (e.g: Gaia, SDSS, JWST, Hubble, Roman, Rubin-LSST). These are analysed using advanced machine learning and data-driven methods. My
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-of-the-art machine learning methods, acoustic sensing can provide valuable insight into a wide range of processes occurring within the built environment. Potential applications include structural health
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diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication, and policy brief writing
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intelligence, or related areas Experience with large language models, machine learning systems, software testing, program analysis, requirements analysis, or software quality assurance Strong programming skills
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Want to teach machines the mechanisms behind how the world changes — and build agents that act on them? Join us! World models are controllable, physics- and mechanism-grounded simulators of reality
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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12.01.2026, Academic staff The Professorship of Machine Learning at the Department of Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13 100%; initial contract 1.5