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of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning
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: excellent university degree (diploma, master's degree) in transport or related study programs with a solid basis in transport, data science, and/or data analytics; or equivalent practical experience
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of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning
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Technologies at TUM’s School of Engineering and Design is looking for a doctoral researcher (f/m/d) in the area of Collaborative Machine Learning for the Energy Transition. You are passionate about machine
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of the operation of helicopter systems. • Confident handling of Python and common data science tools. • Knowledge of high-performance computing and machine learning. • Fluency in written and spoken German and
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from analytical chemistry to material science and engineering. There is no need for previous knowledge in the described fields but a strong motivation to learn and push the boundaries of our current
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/m/d) in Machine Learning Applications to Sustainable Energy Management. You are passionate about applying cutting-edge information technology to solve the energy and climate crisis and would like
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resource economics) or related disciplines strong analytical (i.e. microeconomics, production or resource economics) and methodological skills with a focus on quantitative data analysis (e.g. econometrics
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and deep learning Programming experience with Python and Pytorch Strong analytical and problem-solving skills Excellent communication & interdisciplinary skills Fluency in English (written and spoken
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03.06.2021, Academic staff The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and privacy-preserved