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natural cognitive systems and develops novel methods of human-machine interaction. Our interdisciplinary team combines expertise in computer science, electrical engineering, psychology, and neuroscience
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, regionally anchored top university as it focuses on the grand challenges of the 21st century. It develops innovative solutions for the world's most pressing issues. In research and academic programs
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coupling for a successful energy transition. Our research institute conducts applied research, develops innovative technologies for public and industrial clients, and translates these into marketable
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exciting new topics. You will work in a multidisciplinary team of enthusiastic data scientists, software developers and domain scientists on, e.g.: Developing self-supervised learning frameworks to extract
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reports Develop and apply software tools Document software and data according to the FAIR data principles Contribute to workshops and training activities Your Profile: Master with subsequent PhD degree in
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and socially sustainable agriculture – together with society. ZALF is a member of the Leibniz Association and is located in Müncheberg (approx. 35 minutes by regional train from Berlin-Lichtenberg
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Masterthesis: Evaluating Techno-Economic Feasibility of Chemical Recycling (Solvolysis) for Industry
techno-economic feasibility and governing policy regulations, and to identify potential value chains through a comprehensive market analysis. Aims The ultimate goal is to develop a well-defined strategy
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techno-economic feasibility and governing policy regulations, and to identify potential value chains through a comprehensive market analysis. Aims The ultimate goal is to develop a well-defined strategy
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techno-economic feasibility and governing policy regulations, and to identify potential value chains through a comprehensive market analysis. Aims The ultimate goal is to develop a well-defined strategy
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the possibility of an extension. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and