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-identified scans, records and sensor feeds to answer questions such as: Can we predict a patient’s response to treatment without ever seeing their raw file? Can an algorithm learn the warning signs of trouble
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in simulated environments and with real data on real UAVs. Defining and calculating measures for levels of trust in the developed algorithms is essential. These uncertainty-aware algorithms can self
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argon. The analysis of the ProtoDUNE data will help to validate calibration techniques and particle identification algorithms. The candidate should have a good knowledge of particle physics and experience
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processing, data analysis, data-driven modelling, optimisation and computation algorithms, machine learning models and neural network structures, as well as strong skills and experiences in computational
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Deadline: 31 October 2025 Details This project aims to develop new algorithms for reinforcement learning from human feedback, to effectively solve complex reinforcement learning tasks without a predefined
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their physical optics. At the University of Sheffield, we are rewriting the rules. Our “digital lens” approach fuses novel optical designs with powerful AI and sophisticated algorithms to build microscopes
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dominant paradigm for data analytics is to run a machine learning algorithm on a fixed dataset to generate a single model. The model is then applied in a specific task for forecasting purpose. Such learning