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Field
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or application. Strong technical expertise in one or more of the following areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and
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framework or knowledge graphs; 2) Uncertainty analysis and risk modelling; 3) Port and maritime operations; 4) Cybersecurity or critical infrastructure protection; and 5) Machine learning and real-time data
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using NVIDIA Isaac Sim, and learning-based policy development for manipulation and locomotion tasks. The role will involve both experimental robotics work and AI model development for sim-to-real humanoid
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, including parsing and processing large document corpora. Strong understanding of machine learning or AI methods applied to health or biomedical data. Demonstrated ability to assess model outputs, identify
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of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical systems. Key Responsibilities Derive and analyse closed-form mathematical
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analysis and application of machine learning techniques to structured numerical and unstructured textual data; (d) have a good track record of academic writing, including report writing, manuscript
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knowledge of machine learning models and Python tools for signal processing and machine learning. General knowledge of system architecture and APIs. Previous knowledge of physiological signal processing. 5
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experiences on computer vision • Strong programming skills in using Deep Learning tools like PyTorch and GPU clusters. • Good written and verbal communications. • Open to Fixed Term Contract
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the Electrochemical Engineering Science Group and work within Dr Daniel Niblett's team on the five-year UKRI EPSRC ION-H2 Fellowship. The programme combines computational modelling, machine learning
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex network models to partially observed hospital infection data, leveraging patient movement data. This research will