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and apply machine learning and deep learning models to analyse complex, multi-dimensional datasets, including spatial transcriptomic, proteomic, and single-cell sequencing data. The candidate will
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interpreted using deep learning to estimate tool-to-retina distance and generate accurate three-dimensional navigation commands without relying on conventional 3D reconstruction. Research Objectives
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federated learning and multimodal deep learning models for healthcare. The project will focus on enabling privacy-preserving learning from distributed healthcare data sources, including longitudinal medical
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experience, deemed equivalent by the GRC (or delegate). The ideal PhD candidate will have: A strong background in machine learning, deep learning, and signal processing Proficiency in Python and machine
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should ideally have experience in: Essential Deep learning and machine learning Computer vision Python programming PyTorch or TensorFlow Strong mathematical and analytical skills Desirable Video
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hyper elastic materials 2. Design optimisation for programmed mechanical response Perform size, shape, or topology optimisation Combine with deep learning-based optimisation approach Additive
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surveys and fixed-camera monitoring of cliff faces, rock platforms and debris aprons. Apply change-detection tools (e.g., VoxFall) and deep-learning image segmentation to quantify debris dynamics and cliff
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requiring long-term strategies of building trust to gain access to the object of research. Fieldwork may consist of deep immersion in one place or research in a number of sites – in either case, the onus is