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expertise in machine learning, soil microbiomes, microbial 3D printing and biophysics, our team has access to a broad spectrum of techniques and practical know-how. This is therefore an exciting opportunity
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-learning individual with strong transferable research skills to join our Subtractive Process team at the Advanced Manufacturing Research Centre (AMRC), as a Project Engineer. You'll be instrumental in
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? This PhD project offers a unique opportunity to apply machine learning to solve a critical engineering challenge within the railway industry. The Challenge: Rail grinding is a crucial maintenance activity
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, combining the radio frequency (RF) circuitry of a transceiver with the digital processing needed for machine learning, all in a single microchip. Next generation wireless devices will not only send and
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engineering. Recent advances in large language models (LLMs), such as ChatGPT, GitHub Copilot, and similar systems, have shown that these models can generate computer code from short pieces of text (i.e
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will contribute to developing know-how that can be used to generate a methodology based on physics-informed machine learning models for process development and optimisation. The project will validate a
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experiments and cognitive modelling. You will focus on machine learning, but will be involved in all areas. There are also spinout opportunities. For details: PhD information sheet The team have wide experience
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species, and the emergence of previously unseen classes. Recent advances in remote sensing and machine learning provide new opportunities to address these challenges, but most current approaches
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chemistry, machine learning, movement ecology, RF-engineering, electronics etc). Main duties and responsibilities Devise, develop and test fabrication approaches for the construction of microbatteries in a
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collaboration experience. Main duties and responsibilities Develop findable, accessible, interoperable, and reusable (FAIR) AI / machine learning software, tools, and workflows to support multiple exploratory