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) therapy on the biology of γδ T cells and how can we use this knowledge to help us predict the success of therapy and prevent the development of side-effects. Position 1 will focus on the cellular and
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measured data, apply necessary filtering and selection of data features to be stored. Couple the numerical model and the measured input data to establish a model that can predict the outcome in terms
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research in the field of modern high voltage polymer electrolytic capacitors, develop models for lifetime prediction, methods to predict and test for reliability, understand physics of failures at elevated
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employing an integrative approach involving protein structure prediction by AlphaFold 3 combined with crosslinking/mass-spectrometry and single particle cryogenic electron microscopy on native or recombinant
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could include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success Policy Implications and Recommendations
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results with AI models and system simulations to create a digital twin of the PtX process for predictive optimization and scenario analysis. Funding This PhD position is generously funded through the Villum