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timings) affect the metabolome and proteome of rapeseed seeds. Your findings will serve as molecular fingerprints to support Deep Learning models for hybrid development. Whom we are looking for: An early
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become part of an exciting project at the Senckenberg Museum of Natural History Görlitz (Saxony, Germany). We are looking for a motivated environmental data modeler – data scientist (m/f/d) to support the
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fire weather index and burned area in ERA5 reanalysis over the Mediterranean, (2) these conditions will be tested in CMIP6 models, to (i) check whether the models can reproduce such conditions and (ii
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environment. The position would be an ideal substantive fit for students interested in development, political and economic inequality, and in processing and analyzing large-scale online data. One position is
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experience growing, managing, and phenotyping plants in the field and greenhouse. You have experience using modern approaches in root phenotyping, image analysis, or simulation modeling to understand
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business models. Analyze, develop, and test strategic community-led business models and innovation agendas to support the growth of community-led business cases and enhance scalability and effectiveness
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The Network Analysis and Modelling group investigates how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. Using machine learning and network
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modelling to evaluate additive and interaction components of mixture performance. Your qualifications and skills: Ideally, you are enrolled in a Master’s program in plant sciences, crop science, plant
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changes during fermentation and drying processes Proficiencies in computational process modelling and statistical data analysis Excellent publication record and proven success in attracting third-party
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approaches in root phenotyping, image analysis, or simulation modeling to understand the functional roles of plant roots in agroecosystems. You have excellent communication skills and will to collaborate with