20 algorithm-development-"Multiple"-"Prof" "UNIS" Postdoctoral positions at Leibniz in Germany
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pursues equality for all groups of people. We promote the professional development of women at the institute and strongly encourage them to apply. You can find information on our representative body
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develop research-based recommendations for action for policymakers, business and society. Through our five fields of activity – research, promotion of young researchers, policy advice, participation in
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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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timings) affect the metabolome and proteome of rapeseed seeds. Your findings will serve as molecular fingerprints to support Deep Learning models for hybrid development. Whom we are looking for: An early
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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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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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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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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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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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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