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computer vision is required. Experience of efficient ML techniques, edge AI hardware platforms, low-power computing, earth observation is desirable. They will have excellent programming skills (Python, C
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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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post for a post doc/research software engineer to work on EPSRC grant EP/W030756/2: “aeon: a toolkit for machine learning with time series”. aeon is an easy-to-use, scikit-learn compatible toolkit for a
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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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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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data analysis and machine learning skills. The Research Fellow in Intelligent & Resilient Ocean Engineering – Geoscience will join a large community of scholars at Southampton working across the marine
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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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experience in machine learning, computer vision, and/or big data. Prior experience in generating, curating, or handling microscopy data. Publication record illustrating first author high impact research
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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