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. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum
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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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interdisciplinary research on knowledge extraction from social data. Project description The project is in the emerging area of fair social network analysis. In today’s algorithmically-infused society, data about our
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successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment
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strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. The applicant should furthermore
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research on the development of new inference methods and algorithms for wide classes of stochastic models. However, research will be conducted in collaboration with biologically oriented researches allowing
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research on the development of new inference methods and algorithms for wide classes of stochastic models. However, research will be conducted in collaboration with biologically oriented researches allowing
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and significant piece of information to the right point of computation (or actuation) at the correct moment in time. To address this challenge, you will focus on developing theoretical and algorithmic
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and algorithmic foundations for goal-oriented, semantics-aware communication strategies that enable efficient, intelligent, and adaptive information exchange in joint communication and control. In
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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