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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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Do you want to contribute to groundbreaking research in the development of a theoretical framework and numerical algorithms for evolving stochastic manifolds? This is an exciting opportunity for a
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, 6G communication and wireless sensor networks as well as research and education within life science, health technology, smart electronic sensors and medical systems. The Department of Electrical
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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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industrial robotics, industrial IoT, 6G communication and wireless sensor networks as well as research and education within Life Science, smart electronic sensors and medical systems. The Department
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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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, electric vehicles, industrial IoT, 6G communication, and wireless sensor networks, as well as research and education in Life Science, health technology, smart electronic sensors, and medical systems
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develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates
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are rapidly evolving – ranging from remote sensing and automated sensors to genetic techniques and classical field-based inventories. This PhD project focuses on how biodiversity in forests can be measured
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the following research areas providing a template for relevant directions: - Field Robotics with a focus on Arctic Environments - Autonomous Navigation and Motion Planning in Sensor-Degraded Disaster Environments