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impacted their genomic diversity. The project focuses on Lepidoptera (butterflies and moths) as model system, that are currently threatened or vulnerable, which serve as important ecological indicator. By
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this. The expected amount of lecturing is 20 ECTS credits (15-20 ECTS credits for Assistant/Associate Professors) per year, where 5 credits typically correspond to one lecture course having 28 hours
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integrative part of sustainable and healthy diets. Development of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12
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of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12 PhD candidates will be based across seven different universities
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qualifications support this. The expected amount of lecturing is 20 ECTS credits (15-20 ECTS credits for Assistant/Associate Professors) per year, where 5 credits typically correspond to one lecture course having
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integrative part of sustainable and healthy diets. Development of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12
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of analytical methods, digestion models, dietary modelling, and conducting consumer surveys will form part of the Doctoral Network’s tasks. The 12 PhD candidates will be based across seven different universities
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evolutionary cell biology. Our ERC-funded project uses pigment cells in tropical cichlid fishes as a powerful model to uncover how complex cellular phenotypes and developmental programs evolve. We integrate
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of geological hydrogen reservoirs, combining geochemistry, microbiology, isotope analysis, and modelling. The doctoral researcher will focus on the geological hydrogen aspects of the project by integrating
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning