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Field
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embrittlement, leakage, and long-term material degradation raise critical reliability concerns for flight certification. Structural Health Monitoring (SHM) and Digital Twinning (DT) technologies can address the
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are fundamentally limited by a "one model for one task" design philosophy. This approach incurs prohibitive engineering costs and yields brittle solutions with poor generalisation to new network conditions, trapping
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Fullyfunded PhD Studentship: Underground Pumped Hydraulic Storage Background One lesser considered challenge of the shift towards renewable energy is the need for reliable and robust energy storage
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: Advancements in biosensor technology are at the forefront of modern biomedical research, addressing the growing need for precise, real-time monitoring of biomolecules and overcoming critical challenges in
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
operate in spectrum environments that are scarce, heterogeneous, and highly dynamic, which makes the traditional static and centralised spectrum management strategies inadequate for ensuring reliable, low
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the Southwest. Geospatial and engineering analyses will identify optimal sites and system configurations, while collaboration with the Law School will assess legal and regulatory frameworks, planning constraints
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At the Faculty of Engineering and Science, Department of Materials and Production one or more positions as PhD stipend in Artificial Intelligence for Autonomous Systems, within the doctoral
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Engineering (AME), and in collaboration with the Aerospace Center (AC), Keck Center for 3D Innovation, and Department of Industrial, Manufacturing, and Systems Engineering (IMSE), this position is a cornerstone
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project will develop novel methods for modelling and controlling large space structures (LSSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. Working with leading
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erode trust in model outcomes. This project aims to create a unified framework for building lightweight, data-efficient multi-modal AI models that can effectively handle uncertainty and generate reliable