176 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Technical University of Munich
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(e.g. via machine learning) to qualitative analyses (e.g. via interviews) to support ambitious policies for climate and energy transitions. This position Green hydrogen is key to decarbonizing many hard
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.) Knowledge of machine learning, data mining, or related fields Excellent communication skills and ability to work in a collaborative team environment Interest in social science research methods and theories
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evaluating machine-learning models. Expertise in in the field of Building Information Modelling and geometric modelling is greatly beneficial. Excellent English and the willingness to learn the German language
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of microfluidic devices. Simulation for microfluidics. (CFD) High Performance Computing and/or GPU programming for this domain. Machine learning algorithms for this domain Clean energy solutions (e.g., microfluidic
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on the 1D or analytical model) Hybrid simulation approach (e.g., which combine CFD and 1D simulations) High Performance Computing and/or GPU programming for this domain Machine learning algorithms
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for this position, the candidate should possess in-depth skills in programming and hands-on training and evaluating machine-learning models. Expertise in in the field of Building Information Modelling and geometric
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/MSc degree in bioinformatics, computer science, mathematics, life sciences - background in Machine Learning and/or RNAseq analysis - interest in biological applications - passion for science and
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-year bachelor’s degree in chemistry, chemical engineering or adjacent areas - High motivation and joy to learn about chemistry and chemical engineering - Strong commitment to solve some of the most
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stimulating environment and gain international exposure through our partners and collaborators across Europe and the world. We support career development, continued education, and life-long learning. Situated
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lifecycle. You will be based in the Professorship for Urban Productive Ecosystems (PI: Monika Egerer) at the Technical University of Munich in Freising, Germany. Visit www.upe-lab.de to learn more about the