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Field
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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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Engineering, Chemistry, Biochemistry, or related scientific discipline Experience 1. Familiar with various cheminformatics, bioinformatics, data science, retrosynthesis algorithms for enzymes. 2. Experience in
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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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ideas from machine learning, econometric choice modelling and mathematical psychology. You will investigate how large language models, optimisation algorithms and automated model search methods can
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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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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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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