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wearable devices into the existing remoting monitoring system, and the integration of algorithms for automatic analysis of data received at the backend. About You You should hold a MSC degree in a relevant
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-dimensional digital twin of the atmosphere using a ground-based array of stereo cameras and sensors. The post-holder will contribute across the research lifecycle from dataset curation to the design of novel
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analysis of new and existing large-scale wearable sensor datasets. There will be exciting opportunities to recruit participants and support collection of new wearable camera and accelerometer datasets with
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We invite applications for a full-time Postdoctoral Research Associate to join the new Data-Driven Algorithms for Data Acquisition (DataAcq) project. This is a timely project developing new
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for long-term production projects including large arrays of full-recticle and wafer-size semiconductor sensors, as well as one-off R&D pieces and small commercial activities requiring a rapid turnaround
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
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near to completing a PhD in neuroscience, biology, or a related discipline. • Experience with hardware development, sensor integration, or custom device fabrication. • Experience with
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, delivering tested methods, and creating algorithms to expand MMFM capabilities across domains like cardiology, geo-intelligence, and language communication. The postholder will help lead a project work package
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leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis of normative frameworks and aggregation rules, and
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aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will support the development