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, the CAPeX approach to finding new electrocatalytic materials for energy conversion reactions uses state-of-the-art machine learning techniques, but experimental feedback is needed to improve the models and
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collaboration within e.g. nutrition, chemistry, toxicology, microbiology, epidemiology, modelling, and technology. This is achieved through a strong academic environment of international top class with
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characterization of glycoside hydrolases, and a postdoc working on computational modelling of the same enzymes. The PhD focuses on ligand-observed NMR analyses and other relevant methods to provide insight
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circular, economically viable future for packaging. Through SSbD assessment in collaboration with the consortium, experimental work and risk modeling, you will help uncover the hotspots in the production
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optimization. We are looking for a candidate who is motivated by both technical curiosity and making a real-world impact. Ideally, you: Have experience with AI models (e.g., graph neural networks, supervised