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candidate will be anchored in the Belowground Crop Ecology group in the Crop Sciences section within the Department of Plant and Environmental Sciences, Faculty of SCIENCE, University of Copenhagen (https
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initially limited to 3 years. Research project Advancements in structural biology and AI-guided protein structure design are providing new insights into the structures of receptors responsible for food taste
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in shaping material choices, structural properties, and life cycle performance? How can uncertainty in LCA results during early design stages be modeled and communicated, ensuring that sustainability
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-of-the-art equipment, facilities, and infrastructure. • A competitive salary and social benefits (e.g., health coverage, parental leave, social security, etc.) (https://www.sdu.dk/en/om-sdu/international-staff
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FOR DRAWING UP OF PREDOCTORAL CONTRACTS FOR THE TRAINING OF DOCTORAL STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119865 Development of machine-learning and graph-based models
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support and mentoring for building a career in academia or industry Professional development through JuDocS, including training courses, networking, and structured continuing education ( https://www.fz
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
Your Job: The field of structural DNA nanotechnology holds a great promise for the realization of all-DNA building blocks with arbitrary complexity and shape at the sub-nanometer scale
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the development of buffer structures) and AlGaN growth for AlGaN/GaN heterostructures. Exchange and collaboration with a diverse team of scientists from the REC² Cluster, in particular with those from the fields
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on • developing and realizing an open-topic PhD project in close collaberation with our working group. • PhD studies supported by our faculty's structured graduate program (RIGeL) • An outstanding scientific
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STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119977 Development of mathematical and machine-learning algorithms to support an intelligent, integrated system for biosafety