39 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" PhD positions at Newcastle University
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be hosting a ‘Prospective applicant webinar’ at 2:00pm on the 26th of November. Link to the event can be found here: https://events.teams.microsoft.com/event/376b2195-d8da-47c0-86e2-b18813ec19e3
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: https://events.teams.microsoft.com/event/376b2195-d8da-47c0-86e2-b18813ec19e3@4a5378f9-29f4-4d3e-be89-669d03ada9d8 . Number Of Awards 1 Start Date 1st October 2026 Award Duration 3.5 years Application
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here: https://events.teams.microsoft.com/event/376b2195-d8da-47c0-86e2-b18813ec19e3@4a5378f9-29f4-4d3e-be89-669d03ada9d8 . Number Of Awards 1 Start Date 1st October 2026 Award Duration 3.5 years
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prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained using datasets generated by the high-fidelity numerical solver. The surrogate will emulate key
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equations to simulate pollutant transport, mixing and biochemical processes. To enable rapid prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained
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programmed in advance. If anything changes, it may fail. This project explores how to build more adaptable systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural
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systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural language understanding (to interpret instructions), and action generation (to respond), enabling robots
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, including but not limited to computer science, data science, engineering or mathematics, who are passionate about machine learning and AI research. Strong analytical thinking, problem-solving skills, and the
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machine learning and AI research. Strong analytical thinking, problem-solving skills, and the ability to engage with complex data challenges will be greatly valued. Experience with Python or AI frameworks
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properties of representative sediment classes. · Evaluate methods for predicting sediment type and physical properties from geophysical data using machine learning. · Assess the reliability