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analysis, human centric artificial intelligence, machine learning or large data management. We are seeking candidates whose expertise spans these backgrounds with a track record and demonstrated curiosity
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in Python, experience of machine learning with scikit-learn and the ability to work collaboratively. This position is ideal for someone who has recently or is about to finish their PhD and is
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collaborators and travel to academic conferences and project meetings to present the work. Successful candidates must hold (or close to completing) a PhD in a relevant subject. Knowledge and experience in
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strategic investment by UKRI, and part of the School of Electronics and Computer Science. The role will involve a core focus on AI research (machine learning, multi-agent systems, causal AI, optimisation
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listeners. We are seeking candidates with a Ph.D. (either awarded or nearing completion) or equivalent professional qualification and experience in Machine Learning, Statistics, or a related field, who have
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constantly ingests data from many sources, generates potential adverse scenarios, models and labels the scenarios, and uses new and existing machine learning methods to build intelligent and proactive risk
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with a global sediment database and use remotely sensed and other geographical data with machine learning/Bayesian Modelling techniques to establish drivers of global sediment flux. They will use
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term basis for 36 months due to funding restrictions. As part of your role, you will: Develop novel Bayesian machine learning approaches for psychoacoustic modelling. Publish your findings at top-tier
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with a Ph.D. (either awarded or nearing completion) or equivalent professional qualification and experience in Machine Learning, Statistics, or a related field, who have in-depth knowledge in and
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constantly ingests data from many sources, generates potential adverse scenarios, models and labels the scenarios, and uses new and existing machine learning methods to build intelligent and proactive risk