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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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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
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, the model will maintain a global scope to capture international interactions, such as the demand for agricultural products from regions like the EU. This research will inform the design of effective and
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the professional provision of research and patent information to science and industry as well as the development of innovative information infrastructures, e.g. with a focus on research data management, knowledge
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also undesirable outcomes such as stereotyping and other biases. These biases are reduced when people experience cognitive conflicts. In a DFG-funded project, we aim to test whether cognitive conflicts