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Field
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-terminal antennas and beamforming operating in FR1 bands and future FR-2, enabling robust terrestrial–satellite integration for safety-critical air mobility services. To develop AI-based algorithms
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representation. Key aims include improving the generalizability, interpretability, reasoning and causal grounding of these models, developing new optimisation algorithms with biologically meaningful regularisation
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Analysis for Stable AI” funded by a European Research Council Advanced Grant. The Opportunity: This postdoctoral position will be devoted to research on algorithms in Artificial Intelligence (AI) from
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qualifications will be considered. Experience of using machine learning algorithms and toolsets, ideally in a research context. Strong programming skills (e.g., Python, Java, C++) An interest in physiological
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with key Analysts and the Research Data & Evidence team you will be responsible for managing work areas from the design and development of funding algorithms and quality assurance processes to delivery
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learning tools for the prediction of composite manufacturing processes. You will work on development of algorithms, custom written codes, application of commercial finite element software and development
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Zero transport strategy. Outcomes will include novel AI algorithms, validated navigation architectures, and new insights into next-generation intelligent mobility solutions. The student will undertake
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-Making and Route Optimisation: Develop adaptive algorithms within a bias-aware ensemble Kalman filter framework to propose alternative flight paths dynamically. The system will aim to maximise safety and
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visual inspection. The research will address several challenges: Complex Surfaces: Developing robust algorithms (leveraging Convolutional Neural Networks and Transformers) capable of identifying tiny
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pre-processing), mining dictionary data, and developing novel algorithms for time-sensitive word sense disambiguation (WSD) in Latin, contributing to the creation of a 100-million-token annotated corpus