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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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the University of Liverpool. As well as gaining a formal HE teaching qualification, you’ll learn key pedagogical skills for the academic job market, primarily through shadowing mentored classroom involvement
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the intersection of ecology, machine learning, and sustainable land management, the research will combine field data collection, deep learning model development, and stakeholder co-design to support biodiversity
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sluggish diffusion kinetics of HEAs make them excellent candidates for resisting oxidation and corrosion in high-temperature steam. Guided by thermodynamic modelling and machine learning, we will identify
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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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, 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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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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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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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