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Field
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: Framework Development: Design and implement a generative deep learning framework for cross-modal integration and analysis, resilient to distribution shifts. Correlation Discovery: Identify interpretable
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Overview: Cranfield University and Magdrive, offer a fully funded PhD position under the umbrella of the R2T2 consortium to study the optimisation of their thruster for a kick stage. R2T2 is a UKSA
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: The research project aims to identify the most effective machine learning/deep learning models for modelling normal IoT device behaviour and detecting anomalies in encrypted traffic patterns. Furthermore, it is
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problem-solving skills and deep expertise in the development of complex computational models. Candidates who have not yet acquired their PhD would be appointed at the Research Assistant level. The
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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, analytical and computer programming skills. Advantage will be given to applicants with experience in one or more of the following: signal processing, deep learning, acoustics, psychoacoustics, acoustic
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shift in the world of hardware design. On the one hand, the increasing complexity of deep-learning models demands computers faster and more powerful than ever before. On the other hand, the numerical
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understood how such automation solutions can be safely and robustly supported with state-of-the-art deep learning. There is a need for new AI that can incrementally learn and adapt without losing accuracy
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
Fully funded PhD at Cranfield University, supported by the EPSRC DTP and Rolls-Royce. This 3-year project covers tuition fees, a tax-free stipend, and funding for training, conferences, and a
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an increasingly complex development environment. Areas to consider that impact the modelling are: Framework Language Process How wide / how deep i.e. what do we model and why? How much provides a good answer i.e