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that demand interdisciplinary solutions? Then the Program for Collaborative Doctoral Projects is the perfect opportunity for you. Many of today’s most pressing problems can only be tackled through
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highly motivated PhD student to join a DFG-funded international project that investigates plant - microbiome interactions through large-scale metabolomics and other - omics platforms. Your tasks: Process
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to develop long term, quantitative strategic plans that emphasize sustainable agribusiness enhancement. This PhD position is carried out in collaboration with the Doctoral Program in Agricultural and Forestry
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degree (or equivalent) in Data Science, Computational Biology, Bioinformatics, Computer Science, Physics or a related field Solid programming skills and knowledge in deep learning, statistical modelling
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) with excellent grades in computer science, materials science, physics, or a related discipline Practical experience in data science, including the application of machine learning (ML) methods or large
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 5 days ago
of computational load for such a development. In the frame of a third party funded research project, we are looking for a PhD Student to work on and develop breeding strategies to mine Winter-wheat accessions stored
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that demand interdisciplinary solutions? Then the Program for Collaborative Doctoral Projects is the perfect opportunity for you. Many of today’s most pressing problems can only be tackled through
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Your Profile: University degree (Master) in Physics, Chemistry, Computer Science, or related fields, with an overall grade of at least “gut” (or equivalent, e.g. cum laude) Expertise in quantum mechanics
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• Scientific publishing Your qualifications: • Completed academic university degree (university diploma / M.Sc.) in Computer Science, Geoscience, Physics, Data Science, or comparable subjects • Experience in
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1