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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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mathematical and computational engine of Artificial Intelligence (AI), and therefore a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world. Integreat
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information at individual level, with specific attention to open and reproducible research, e.g., in the development of codes and algorithms. We will focus on devising computational solutions that can
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candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame factorization methods
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subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning technology, HCI, CSCW, IT operations and applied data processing
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environment in Norway, and offer a wide range of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software
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the next five years. SURE-AI is a Norwegian AI center funded by the Research Council of Norway (2025-2030). The primary objective is to create a new generation of algorithms for inference and decision-making
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with algorithms for wearable data University of Manchester (UK): To learn mathematical modelling of hormone rhythms. University of Bristol (UK): To learn mathematical modelling of hormone rhythm
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constraints, focusing on long-term reliability and autonomy. Robust operation and control of decentralized PV-battery systems: Explore control algorithms and operational approaches that maintain stable
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for teaching activities. About SURE-AI SURE-AI is a Norwegian AI centre funded by the Research Council of Norway (2025-2030). The primary objective is to create a new generation of algorithms