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at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will involve designing innovative QML approaches and collaborating
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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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machine vision algorithms. The system will be designed with the physical constraints of remote fusion environments in mind, including radiation tolerance, restricted access, and the need for automation and
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high-throughput screening using genetic and imaging tools. Apply molecular and cellular assays to study inflammatory processes. Analyse integrative omics datasets using bioinformatics and machine
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the results of which would be used to enrich the available experimental data in order to develop a Design for Manufacture and Performance concept based on machine learning algorithms where the required
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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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high-throughput screening using genetic and imaging tools. Apply molecular and cellular assays to study inflammatory processes. Analyse integrative omics datasets using bioinformatics and machine
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. This project uses zebrafish as a model to identify the signals that recruit regenerative cells to the site of injury. Genetic and pharmacological inhibition of signalling pathways will be used to identify key
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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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, including how to guarantee the properties of stability and constraint satisfaction while probing the system and learning a new model. This project aims to develop novel algorithms for the adaptive distributed