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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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-scale metagenomic assembly and genome recovery • Comparative genomics and molecular evolution • Machine-learning-based protein prediction • Data integration, bioinformatics and phylogenetics • Scientific
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research experience in AI, machine learning, or NLP Published work in reputable conferences or journals Outstanding academic performance in relevant modules or degrees A strong motivation to work on cutting
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interaction, signal processing, data science and machine learning. The successful candidate will gain expertise at the intersection of structural health monitoring, railway engineering, and advanced artificial
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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, or other related academic discipline. Good programming skills (preferably Python). Background/work experience in Cyber Security, Machine Learning, and Finance would be highly beneficial. How to apply
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, machine-learning tools, and Lagrangian transport modelling. You will be based at the British Antarctic Survey and work closely with experts at the University of Leeds and Exeter, who provide cutting-edge
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contribute significantly to these growing fields. This PhD position is ideal for candidates interested in the following areas of machine learning: Geometric learning: exploiting the structure of data (e.g