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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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for hypergraphs and partially ordered sets (POSets), funded by the Swedish Research Council. This project is concerned with saturation problems for two classes of combinatorial objects: hypergraphs and posets
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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partner or with another humanoid robot to solve a spatial problem (e.g. 3D puzzle, fold a paper). The tasks to be carried out are: (i) scene understanding: detection of objects and relations between objects
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model system to validate the findings from the bacterial assays and provide a proof of concept for NP-induced mutational and epigenetic effects in higher organisms. A translational objective is to compare
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biology, biomedical data science or a closely related field. ~5 years of postdoctoral research experience with a strong track record in pathogen/microbial systems (e.g., stress responses, niche adaptation
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salaries for PhD students and postdoc positions. The Fellowships are tenure-track positions with the host organizations, which assume the responsibility for the long-term faculty appointments following a