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Field
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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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experience with implementing machine learning algorithms. Experience or interest in innovative control systems engineering including IEC 61499 would be an advantage. The candidate will be immersed in a
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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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. Analysis of images will investigate the efficacy of manual digital approaches (e.g., Dot Dot Goose) and the development of a marine litter characterisation and quantification algorithm for automated analysis
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programming tools (e.g. OpenMP, MPI) Accelerator programming (e.g. CUDA, OpenCL, SYCL) Serial and parallel debugging and profiling Parallel numerical algorithms and libraries. System software stack
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algorithms based on neural activity data (local field potentials, LFPs) from key deep brain stimulation targets including the basal ganglia and thalamus. Auxiliary data available to implanted devices include
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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
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LEO, MEO, and GEO constellations), and complementary on-board sensors. Research will investigate algorithms for robust multi-sensor fusion and positioning assurance. A strong emphasis will be placed
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modelling are essential. Experience with healthcare data, algorithmic fairness, or deep learning for biomedical data will be advantageous. The successful candidate will contribute to high-impact publications
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implementing innovative signal processing algorithms for radar sensing and communication, establishing clear research objectives, and engaging in the collaborative projects. In addition, the position involves