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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and
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to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both
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that yield valid statistical conclusions (inference) on causal effects when using machine learning algorithms and big datasets. The project is part of the research environment Stat4Reg (www.stat4reg.se ), and
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questions include automated modeling and model simplification/refinement supported by generative AI, system identification, and 3D reconstruction algorithms. Additionally, the research involves developing
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interests, motivation for applying for the position and envisioned contribution to UMA, UmArts, and UCGS research environment (max. 2 pages). Curriculum Vitae with a list of published work including journal
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collaboration with Uppsala University and Linköping University. Qualifications To be admitted to doctoral studies, the applicant must meet the general and specific eligibility requirements listed below and be
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vitae (CV), including a list of previous publications, theses, and/or software outputs (if applicable). Copies of all Bachelor’s and/or Master’s degree certificates, including documentation of completed
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including education details, a list of previous and current positions, and a publication list. Copies of relevant degree certificates. A cover letter describing your research experience and interests, and
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into how algorithmic systems influence the circulation of information and disinformation across digital platforms, and how such processes affect perceptions of credibility, truth, and democratic
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and MSc theses and other relevant publications, if any Documentation and description of other relevant experiences or competences. List of two references with their contact details. The application must