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Field
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, support of individual career plans, advising offers, and international funding opportunities. Research Around the World – Mobility Funds to Let You Internationalise Your Research! Our own funding lines
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and secondments. • Blended Learning Approach: Our training combines intensive in-person workshops at partner institutions with regular interactive online seminars, journal clubs, and research
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Degree Dr rer nat (Doctor rerum naturalium) PhD (Doctor of Philosophy) Course location Ulm Teaching language English Languages Our international PhD programme is only offered in English. Programme
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 3 months ago
systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal
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: principal investigator dr. Lennart de Nooijer) at the Royal Netherlands Institute of Sea Research (NIOZ) is looking for a highly motivated PhD student with a background in cell biology/ (bio)geochemistry
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In cooperation with Max Planck Institute for Biological Intelligence Ludwig-Maximilians-Universität München Technische Universität München Teaching language English Languages The language of
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not least, provide new insight for designing efficient strategies for teaching and learning. Students who have completed the doctoral programme will have the ability to communicate across these diverse
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contribute 1) to the analysis methods and metrics for understanding the complex interactions between forage resource and dynamics; 2) to develop Machine Learning methods for analysing sensor data on animal
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and cell-based basic pharmacology. Collectively, CPPEM has a strong background in exposure to environmental chemicals, pharmacoepidemiology, pharmacogenetics, pharmacokinetics, drug-drug interactions
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predict food-effector systems. Key Responsibilities • Develop graph-based (multi-)omics analysis algorithms • Benchmark graph-theoretic against graph-ML approaches • Analysis of food-related (multi-)omics