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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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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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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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or theoretically oriented. Within the framework of this position, the doctoral student will have considerable scope to develop their own research project. Since 2001, The gender research school has offered co
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barrier in the planned KBS-3 concept for final disposal of spent nuclear fuel. The PhD student will develop, apply, and combine theoretical molecular dynamics (MD) simulations with experimental techniques
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different backgrounds and perspectives lay the foundation for learning, creativity and development. We welcome people with different backgrounds and experiences to apply for the current employment. We kindly