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Field
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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inference. The role will bridge rigorous theoretical work with hands-on algorithm design and development on real-world datasets. The core responsibility is to build and validate federated causal inference
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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computational cognition. The successful candidate will work at the intersection of AI research, cognitive modelling, and human–machine interaction, contributing to the design of algorithms that incorporate
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Full-time: 35 hours per week Fixed-term: 30th June 2026 Applications are invited for research associate positions in Algorithms and Complexity, funded by the European Research Council (ERC
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considered for this project. Application form Closing date 14 March 2022 Decision date May 2022 Additional information ¿Multi-criteria Automatic Algorithm Configuration under Streaming Problem Instances
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numerical algorithms or high-performance software for scientific computing? Do you have expertise in numerical linear algebra or experience with large-scale computing and want to broaden your skills? Would
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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing
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acquired experimental data. These calculations, complemented with chemoinformatic studies, will be used to subsequently develop machine learning algorithms to predict these properties and ultimately estimate
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digital twins using prediction-powered inference to enhance reliability assessment; The theoretical analysis and algorithmic development of methods rooted in statistical learning theory, multiple hypothesis