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-funded project led by Dr. Jagmohan Chauhan in an exciting area of embedded machine learning. The post will be based at the University of Southampton. You will be working with Dr. Jagmohan Chauhan (PI), a
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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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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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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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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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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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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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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