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of targeted delivery strategies of oligonucleotide therapeutics to B cells Host institution: Uppsala University, Department of Pharmacy Supervisor: Prof. Sara Mangsbo, Uppsala University Project 2: Delivery
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medical applications. Federated Bayesian learning offers a solution to those problems by allowing multiple participants to train machine learning models collaboratively, without sharing any data. Bayesian
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, identified key markers and regulators of disease progression are evaluated functionally in various human cell and tissue model systems to assess their potential as treatment or vaccine targets. Here you can
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insights that inform biodiversity management. The project includes: · Apply of deep learning models to annotate bird and bat species from sound recordings. · Develop a Bayesian statistical
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inform more targeted prevention strategies. Key objectives include: Analyzing Inequality and Prevention: Conduct empirical research to assess how prevention policies and healthcare innovations affect
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efficiency (NUE) of trees. In addition to targeting growth of trees, it is important to consider the qualitative properties of wood that are likely to be influenced along with the changes in NUE. In
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an Associate Researcher to join a project aimed at identifying new target molecules (receptors) on leukemia stem cells, characterizing their function, and developing antibodies directed against these targets
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motivated and enthusiastic doctoral student in the field of chemistry to work on the development of cancer-targeting diagnostics. The project is extensively integrated with other groups and PhD students in
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and approaches, as well as implementations that can be practically used. The first project targets specifically numerical programs that appear widely in e.g. safety-critical (embedded) systems, data
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-target interactions, and optimize them towards validation as preclinical platforms alternative to animal model for drugs screening and development. The project is part of a collaborative effort and will