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projects. Integrating environmental, engineering, and social science methods, the interdisciplinary team of researchers that this PhD will augment have identified, evaluated, and are evolving marine litter
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conduct cutting-edge research on topics including, but not limited to: Complexity theory Quantum algorithms and complexity Sublinear algorithms Interactive proofs, PCPs, and zero-knowledge proofs
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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This PhD project focuses on advancing computer vision and edge-AI technology for real-time marine monitoring. In collaboration with CEFAS (the Centre for Environment, Fisheries, and Aquaculture
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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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Applications are invited from PhD studentship candidates with good first degrees in computer science, physics, maths, biology, neuroscience, engineering or other relevant disciplines to join
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The PhD studentship will be based at the University of Cambridge in the Department of Materials Science and Metallurgy as part of the Structural Materials Group. The Structural Materials Group is a
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Deadline: All year Round How to apply:uom.link/pgr-apply UK only This PhD studentship is open to Home (UK) EU applicants with pre-settled status (funded by TWI Ltd). The successful candidate will
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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based