581 developer-"https:" "https:" "https:" "University of Aberdeen" PhD positions in Germany
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of BIG-NSE, UniSysCat set out to recruit the most talented students and early-career researchers worldwide, offering them a broad, high-level structured educational programme to prepare them
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institutions, and a provider of research and development for companies throughout the world. The INM is an institute of the Leibniz Association and has about 250 employees. INM’s research group Immuno-Materials
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-funded Research Training Group ReMPro, further fostering scientific collaborations and career development. Specific Requirements a completed academic university degree in a relevant field (e.g
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region. Our broad range of subjects, the dynamic development of our main research areas and our central location in Cologne make us attractive for students and researchers from around the world. We offer a
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pharmacological research. Among these approaches, MINFLUX represents a particularly powerful recent development. MINFLUX is a revolutionary microscopy technique that enables unprecedented optical and temporal
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, analyze existing datasets and extract results using advanced data analysis workflows Work at the interface of instrumentation development, cutting-edge experiments, and high‑performance data science
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Degree Dr rer nat Course location Münster Teaching language English Languages English only Programme duration 6 semesters Beginning Only for doctoral programmes: any time Application deadline https
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Beginning Winter semester Application deadline All students – online application: 1 March for the following winter semester https://www.lmu.de/psy/de/studium/doctoral-training-program-in-the-learning-sciences
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on the development, pathophysiology and control of the cardiovascular system as well as exploring causes, consequences and therapeutic approaches for cardiovascular diseases. The research spectrum of this programme
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 2 months ago
the form of graphs to analyze and predict food-effector systems. Key Responsibilities Develop Probabilistic Machine Learning Models to integrate graphs and food-related omics data Multi-omics integration