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Paul Goldberg and will work on the EPSRC-funded project “Driving Behaviour in Multi-winner Voting” within the Algorithms and Complexity Theory research theme. The position is for the two-month period of
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models. However, rigorous methods for evaluating XAI algorithms are currently lacking. This is in large part due to a gap between how explainability is evaluated within computer science and philosophy. In
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This position focuses on advancing the mathematical and computational foundations of interpretable algorithms for dynamical systems and explainable AI. The successful candidate will develop new
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navigation, trajectory planning, and point cloud mapping; and (3) deploy the developed algorithms on a physical mobile robot operating in GPS-denied, low-light, and geometrically repetitive environments
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). The selected candidate will perform a range of research activities focused on developing new AI algorithms and hybrid classic-quantum approaches for drug discovery under the supervision of the Principal
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validate wearable data algorithms, software tools and pipelines supporting large-scale, multi-cohort research. It will contribute to the ProPASS federated data analysis platform, including testing
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validation of machine learning and artificial intelligence algorithms for biomarker discovery and treatment selection in gynecologic cancer. The incumbent performs a variety of moderately complex technical
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of Computer Science. The post-holder will report directly to Professor Paul Goldberg and will work on the EPSRC-funded project "Driving Behaviour in Multi-winner Voting" within the Algorithms and Complexity Theory
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Rossier RG at INL. Job Duties The job duties will be the following: Analog quantum algorithms for quantum search and optimisation based on quantum Hamiltonian evolution. Quantum algorithms for simulation
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sustainable manner. Key Responsibilities: Responsible for developing explainable machine learning algorithms for Tunnel Boring Machine (TBM) tunnelling and excavation Developing large language model enhanced