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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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We are looking for a PhD student to carry out ground-breaking research with the aim of developing new perovskite oxide materials for energy conversion and chemical processes. The project is
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. Subject description Computer Science includes research on algorithms, data structures, computing models and software engineering for the development of resource-efficient, distributed and intelligent system
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targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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targets the development of advanced grey-box modeling frameworks for multiphase flow systems, combining mechanistic, multi-scale flow models with data-driven inference and uncertainty quantification
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to continuation as a researcher at Ericsson Research. Practical work tasks include: Developing algorithms and models for dynamic spectrum sharing using RDT data Implementing and evaluating signal processing and
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to pioneer novel research opportunities enabled by one of the brightest sources in the world, ii) developing AI+Physics end-to-end reconstruction algorithms that will enable a new regime of spatiotemporal
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and data-based models for describing complex materials and (re)active molecules with a focus on their interfaces. Development and implementation of new methodologies and algorithms for simulating
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-breaking research with the aim of developing new perovskite oxide materials for energy conversion and chemical processes. The project is multidisciplinary, and you will work together with leading researchers
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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