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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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opportunity to be an active part in the establishing of our department Comprehensive training programs and individual opportunities for personal and professional development Access to a strong research network
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Your Job: We are looking for a researcher to develop and apply machine learning models for genomic data in our lab. We focus on sequence analysis, genomics, semantics, and cross-domain data
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opportunity to be an active part in the establishing of our department Comprehensive training programs and individual opportunities for personal and professional development Access to a strong research network
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on quantum dot qubits Good software development skills Experience with sub-Kelvin cryogenic techniques Strong aptitude for mentoring students Good communication skills A strong drive to conduct ambitious
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of green hydrogen by use case. Beyond the economic feasibility, a particular focus of this position will lie on the evaluation of different development pathways when deploying green hydrogen infrastructure
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wellbeing. They will use modeling approaches to combine natural and social science data. You will focus on developing and implementing mixed modeling approaches to test pathways through which structure
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to the research and development of dexterous end effectors for a new 6G-based teleoperated surgical robotics system. This position stems out of the large scale 6G-Life project (https://6g-life.de/) and will give
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in the vicinity to the vibrant city of Munich, very well connected by train (ca. 30 minutes), and a perfect starting point to explore the Bavarian Alps and the rest of Germany. Working hours are
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scientific work. After an orientation period, you will have the opportunity to take over the leadership of a team in the EV Lab. Your professional and private development is very important to us and will be