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the Section for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and
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Fabrication of simple electronic and nanophotonic devices Optical and electrical characterization of quantum emitters Defect modeling and identification The PhD research fellow will be affiliated with the Solid
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mammalian cells, organoids, and animal models, and employ a range of approaches, including cell culture systems, diverse molecular techniques, and advanced imaging. The project will involve both internal and
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and analyses in at least one of following languages: R or Stata is required. Fluent oral and written communication skills in English Desired qualifications Experience with longitudinal, register-based
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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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and their interactions with peptides. This primarily includes small angle X-ray and neutron scattering (SAXS/SANS) along with theoretical model analysis including the use of multi-scale and artificial
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data with good utility. This project involves development of deep learning based synthetic data generators that obtain both good utility and protection of privacy, through tailored model approximation
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, sensitivity to modeling choices, and complementary empirical tests as essential components of trustworthy unsupervised learning in high-dimensional settings. Causality will be used as one analytical lens
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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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, in CPDM Laboratory (Physico-Chemical Behaviour and Material Durability) and EMGCU (Experimentation and Modelling in Civil and Urban Engineering), in MAST Division (Materials and Structures