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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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approach for depression in young people - Training for Awareness Resilience and Action (TARA) - with standard treatment (ST). The TARA-group was shown to be superior at six months follow-up and the safety
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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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providing interaction with researchers from diverse research fields and access to various scientific and technical expertise. Background and description of tasks We are developing genetic screening tools
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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