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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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health, and bioinformatics. You will apply advanced AI methods - from classical machine learning to large language models and agent-based AI - on large-scale healthcare datasets, including structured
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. System Integration: Integrating the DTs into material and energy balance equations
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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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with, cloud computing and virtualisation technologies Familiarity and hands-on experience with machine learning techniques desirable Desirable to have work experience (through internships or similar) in
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sustainable heritage management decisions (particularly in an African context), using advanced methods in satellite imagery analysis, remote sensing and machine learning, combined with geospatial analysis
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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to learn laboratory methods for analysis of relevant BGC parameters. Training: You will be based in the Polar Oceans Team at British Antarctic Survey, a highly active research team focused on both
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at leading international conferences and publish in top-tier journals. The successful candidate will gain advanced expertise in multi-sensor fusion, signal processing, machine learning, and positioning
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data