22 algorithm-development-"Multiple"-"Prof" Postdoctoral positions at Leibniz in Germany
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development opportunities and annual performance reviews. You are paid according to the collective agreement for the public sector (Tarifvertrag des öffentlichen Dienstes, TVöD Bund), which includes an annual
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, and energy systems into a comprehensive bio-based circular economy. We develop and integrate techniques, processes, and management strategies, effectively converging technologies to intelligently
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development. It is one of the world's leading research institutions in its field and offers natural and social scientists from around the world an inspiring environment for excellent interdisciplinary research
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institutions, and a research and development provider for numerous companies throughout the world. The INM is a member of the Leibniz Association and has about 250 employees. The INM Research Department Energy
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research on industrial policy, developing new insights and contributing to academic and policy discussions. Providing research-based policy advice, translating complex economic analyses into actionable
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project is privately funded by the Werner Siemens foundation. The project focuses on 4D in-wound bioprinting for cartilage repair. The TriggerINK aims to develop materials that support chondrocytes in
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with a focus on transnational terrorism or related topics in international academic journals and with well-established publishers; Development of grant applications and implementation of research
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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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for the compatibility of family and career Support for career development A positive working atmosphere in a supportive team Good access to laboratory resources and to data of the Dortmund Vitalstudy An interesting and
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team to work on machine learning-supported rapeseed genomics and breeding. Your tasks: You design, train and interpret deep-learning models to predict regulatory gene variants in rapeseed genomes. You