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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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-year postdoctoral position aimed at the development of a novel organic-electronics technology, termed a light-emitting electrochemical cell (LEC). The LEC is currently attracting increasing interest from
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electronic characteristics. The project’s goal is to develop fundamental understanding and innovative fabrication processes to solve urgent problems in organic electronic devices, and to enable new components
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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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with big datasets: towards methods yielding valid statistical conclusions” led by Professor Xavier de Luna and Tetiana Gorbach (Statistics). The overall purpose of the project is to develop novel methods
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analysis of complex, longitudinal, and high-dimensional data (e.g., immunometabolic profiles, clinical data, biomarkers). Development and application of predictive models and algorithms for diagnostics
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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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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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nationalities, backgrounds and fields. As a PhD student working with us, you receive the benefits of support in career development, networking, administrative and technical support functions, along with good
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explores novel methodological approaches using survey and register data. Work tasks The work will focus on exploring and developing quantitative approaches grounded in intersectionality theory and with