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to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device (SFDI) and also from our custom-built photoplethysmography (PPG) sensor
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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
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https://www.sems.qmul.ac.uk/research/studentships/674/net-zero-natural-resource-energy-and-time-efficient-manufacturing/The PhD work will benefit from access to unique facilities including
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research environment to grow as a research leader. We will support you in future funding proposals & applications and fully include you in any collaborations/networks. Opportunities for professional
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funding proposals and applications and fully include you in any collaborations/networks. Opportunities for professional development, e.g. through the award-winning Early Career Researcher Institute
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to a net zero economy, electrical power systems are rapidly decarbonizing supply whilst electrifying carbon-intensive demand. There is therefore an acceleration in the connection of low carbon
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Systems, or a related field. Strong analytical and critical thinking skills. Strong machine learning (ML), computer vision (CV), large language models (LLM) for quantitative data, texts, images, and sensor
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diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing . Further Information: This is a full-time and a fixed term
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theory and real-world applications, supporting the UK’s transition towards a renewable, inverter-based power system. The project is funded by Scottish and Southern Electricity Networks (SSEN) and you will
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real-world applications, supporting the UK’s transition towards a renewable, inverter-based power system. The project is funded by Scottish and Southern Electricity Networks (SSEN) and you will work