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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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application of the new methods will be co-designed with the candidate to best match their research interests. As a PhD-candidate at the Department of Earth Science, University of Bergen, you will also be
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big data and advanced methods for modeling exposures, we will establish real-world patterns of hormonal contraceptive use and mental disorders, and identify potential moderating factors such as
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issues will get particular attention. First, identify the most appropriate environmental, economic and societal impact categories to include in the assessment. Second, develop the three models
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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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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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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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of department. Various research topics can be explored, depending on the applicant’s interests. Possible themes include: 1) the changing Meridional Overturning Circulation, from models and in situ data
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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