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of computational optimisation, machine learning, and experimental design of new chemistry. You will develop Bayesian optimisation and other active learning approaches for experimental design with a focus on real
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scales, lubrication and wetting, biophysics as well as machine learning or metamaterials. For experimentalists, we particularly interested in applicants aligned with our existing research infrastructure
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(KCL, London, UK) but will also have the opportunity to travel and work at the Centre for AI and Machine Learning (ECU, Perth, AU) and the School of Psychiatry and Clinical Neuroscience (UWA, Perth, AU
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of subjects such as computer vision, Machine Learning, Artificial Intelligence and Kinematics and dynamics. Autonomous systems, Robotics and Automation. Industry 4.0 and Internet-of-Things. Advantageous to have
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validation with end-users. The student will have access to specialised training in quantum security and advanced machine learning. The self-funded nature of the project affords the unique flexibility to pursue
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tissues • The development of computational methods ranging from image processing to machine learning to analyse, segment, model, and quantify large-scale time lapse datasets Main duties
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, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental scientists and contribute to translational advances in synthetic biology and
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of AI and machine learning to networking, robotics and cyber security and the technologies needed to support AI and intelligent infrastructures. Biomedical Engineering, including bioelectronic devices and
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. This role will be engaged in the development of educational thinking, practice and research in the broad areas of curriculum design, learning and teaching, and assessment. As well as this, the successful
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vision, robotics, machine learning and natural language processing (CSRankings) and were 7th in the UK for REF2021 outputs in Computer Science research. CVSSP and Computer Science are at the core of the