43 phd-studenship-in-computer-vision-and-machine-learning PhD positions at Cranfield University
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training programme with emphasis on innovation and impact, collaborative working and learning, continuous development, active engagement with partners and stakeholders and inclusion of student-led activities
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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funded studentship is part of the Connected Waters Leverhulme Doctoral Programme, which is funding up to 18 PhD studentships to conduct multidisciplinary research on freshwater ecosystems, across two
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, multidisciplinary PhD research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle
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for industrial placements, overseas travel and access to world leading experimental facilities. More details about the WIRe CDT programme can be found at www.cdtwire.com (PhDs under this scheme are for a duration
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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and airport research facilities at the school of Aerospace Transport and Manufacturing at Cranfield University. At a glance Application deadline26 Mar 2025 Award type(s)PhD Start date29 Sep 2025
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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This PhD project will focus on developing, evaluating, and demonstrating an intelligent solution of diagnosis and prognosis for rotating machinery to enhance safety, reliability, maintainability and
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems