18 condition-monitoring-machine-learning Fellowship positions at UNIVERSITY OF SOUTHAMPTON
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Are you keen to pioneer machine learning models that address the challenges of robot perception? We are recruiting a research fellow who will work on our EPSRC-funded research project on “Active
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spiking neural networks, alongside autonomous learning approaches. Develop approaches to optimise the structure of a given artificial spiking neural network, to support their deployment onto state
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modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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manufacturing. You should have interest in or experience with data-driven methods, including machine learning, Python programming, or data curation. Regular reports of research progress are required and research
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in distributed database systems, information retrieval, computer networking or semantic web. The post does not involve working outside of the UK for over 30 days in a row or over 90 days in a year. For
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bioelectronic system that seamlessly integrates therapeutic functions with continuous monitoring capabilities. The system incorporates cutting-edge technologies in bioelectronics, soft robotics, and materials
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complex health and social care challenges, particularly in the management of long-term conditions. We combine AI and traditional epidemiology with qualitative methods to develop impactful, real-world
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
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responsible for maintaining high quality research procedures and will work as part of the team and liaise with three recruiting sites in setting up the study, monitoring participant recruitment, data collection