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vehicles such as lipid nanoparticles (LNPs) • Create experimental protocols for cancerous, healthy human and microbial model cell membranes. • Establish predictive models for peptide-induced transport
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synthetic data generators that obtain both good utility and protection of privacy, through tailored model approximation, as well as new measures of privacy and fairness to be used for assessing properties
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unified and assumption-aware perspective on evaluation, emphasizing robustness analysis, sensitivity to modeling choices, and complementary empirical tests as essential components of trustworthy
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work on adapting or developing marine foundation models. Self-supervised learning and active learning are also possible research topics. You can also focus on challenges related to modelling physics
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in the μ-opioid receptor. The PhD candidate will: • generate high-resolution models of wild-type and R181C mutant μ-opioid receptor based on existing crystallographic data and perform molecular
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impact categories to include in the assessment. Second, develop the three models of Environmental (E-LCA), Life Cycle Costing (LCC) and Social Life cycle (S-LCA) individually, and integrate them
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learning methods for generating complex structures for generation of virus capsids and therapeutic proteins for gene therapy. The project will involve both physics-based forward modeling of protein
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candidate to develop Machine Learning models and frameworks for time series analysis, aimed at understanding how the human brain encodes information. This cross-disciplinary project is a high-level
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of hypothermia with important medical implications. To study this, the Ph.D. candidate will use mammalian cells, organoids, and animal models, and employ a range of approaches, including cell culture systems
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project will be part of the psychology work package which through longitudinal cohort studies with registry linkages and advanced causal modelling techniques will examine the causal relationship between HC