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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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, Psychology, or a related field, to be awarded before March 1st, 2026. Essential skills include an ability to code (e.g., Python, R) and interpret data, knowledge of machine learning and statistics, and a
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cell and spectroscopic analysers. Programming (e.g., R, Python) and machine learning for advanced atmospheric time-series analyses. Skills for presenting research at conferences and writing peer-reviewed
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framework that compares and blends complementary paradigms of physics informed machine learning (such as PINNs, ODIL)—to (i) super-resolve experimental data, (ii) infer unknown parameters such as the
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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ecosystem services such as carbon storage (1-4). Recent advances in satellite observations and machine learning provide novel opportunities to study extreme fires on a global scale. In a changing climate
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of the workflow. While the majority of the project is computer based, there is a small lab-based component in order to generate cell samples to be able to acquire the NMR data. Once proof of concept has been
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avenues may include linking plankton size patterns to krill dynamics, carbon export or nutritional quality, or developing tools for rapid ecosystem monitoring using machine-learning approaches
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Matias, Adrián Villaseñor, Rowena Jacobs Topic 4-Machine Learning for Causal Inference in Health Policy Assessment for Decision Making Supervisors - Dr Julia Hatamyar and Professor Andrea Manca Topic 5