20 machine-learning "https:" "https:" "https:" "https:" "U.S" PhD positions at Loughborough University
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on the International website (http://www.lboro.ac.uk/international/applicants/english/ ). Funding information: Studentship type – UKRI through Flood-CDT (https://flood-cdt.ac.uk/ ). The studentship is for 3.5 years and
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minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/ ). Funding information: Studentship type – UKRI
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relevant subject. English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk
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from https://centa.ac.uk/apply/ ) under the ‘Our project-based studentships’ section on that page. All applications should be made online . Under Campus, please select ‘Loughborough’ and select the
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. Current XAI methods are often generic and overlook industrial realities. This project will embed user-centric explanations directly into machine learning workflows using structured, ontology-driven
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, programming, signal analysis or machine learning are particularly valuable. If you are keen to apply technology to improve global healthcare, we would be delighted to hear from you. Entry requirements
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under multiple environmental and socio-economic scenarios. You’ll develop sought-after skills in geospatial analysis, hydrodynamics, sediment transport, machine learning-assisted detection, and hydro
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of real-time adaptive 3D inspection, dynamically adjusting its measurement strategy based on data quality as well as environmental and scene cues. Positioned at the intersection of robotics, computer vision
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Applications are invited for an employed Doctoral Candidate to be funded by the Marie-Skłodowska-Curie Learning Network for Decentralized critical Infrastructure Asset Monitoring and coNDition
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datasets across variable illumination, wave states and turbidity, enabling hybrid deep-learning and iterative optimisation solutions with clear lab-to-field transferability. Entry requirements: We