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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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contribution to co-operation within the network. The tasks listed are not exhaustive and further activities and tasks may arise in the course of the doctoral thesis in order to achieve the research objectives
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at the Department of Chemistry at Freie Universität Berlin starting October 1, 2025, in one of the following projects: Job description: Contribute to research in one of the following projects. Please, indicate your
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organization for more than 12 months in the three years imme-diately prior to the recruitment date. Compulsory national service and/or short stays such as holidays are not considered. Selection process The
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, using techniques such as: High-dimensional data mining Tensor decomposition Causal inference Statistical process modeling Machine Learning Applications include public transport, private vehicles, traffic
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plasticity in intestinal regeneration and cancer to contribute to the preparation of scientific publications and presentations to contribute to teaching in Biomedical Sciences (Master programme) Profile: M.Sc
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equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep learning, or signal processing; familiarity with
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/or short stays such as holidays are not considered. Selection process The selection and recruitment process will be in accordance with the European Charter and Code of Conduct for the Recruitment
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 7 days ago
breeding populations to investigate the genetic basis of traits such as visual anther extrusion, heading date, plant height, and grain yield. You process genotypic data, ensure its alignment with phenotypic
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biomedical engineering, bioinformatics, biotechnology, molecular imaging and biomaterials. Training within the RegSci PhD programme takes place across three levels: students carry out their own research