55 algorithm-development "https:" "FCiências" Fellowship positions at UNIVERSITY OF SOUTHAMPTON
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Applications are invited for Research Fellow position within the School of Electronics and Computer Science at the University of Southampton. The work will involve the development of methodologies
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the full complexity of fabrication processes and enable optimisation before physical manufacturing begins. This project aims to develop advanced deep learning models capable of predicting fabrication
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Renewable Energy Hub. It aims to develop a novel glass fibre-reinforced polymer (GFRP)–steel–concrete tower-to-monopile transition structure for offshore wind turbines, with a focus on improving structural
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is on a fixed term basis till the end of February 2027. This position focuses on the development of innovative microfluidic-based sensors for continuous, in-situ chemical and biochemical analysis
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to experimental campaigns and will use image-derived structures (including XCT images where available) to develop mechanistic, validated mathematical multiscale models. We are recruiting a Postdoctoral Research
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including anechoic chambers and controlled experimental environments, ensuring high-quality, repeatable data. Develop and apply signal processing workflows for weak transient signals, long-term monitoring
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organisational skills to develop their expertise in dementia research. Applications are invited from enthusiastic early stage researchers to join the research team based at Clinical Neuroscience within the Faculty
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components for innovative-lasers applications. We’re looking for an enthusiastic postdoctoral fellow to be part of our team to advance our Rapid Pulsed Laser Deposition (RPLD) technology and ultimately develop
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Research fellow in dementia research This is an excellent opportunity for a postgraduate candidate with strong research and organisational skills to develop their expertise in dementia research
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) at the University of Southampton. The successful candidate will contribute to an interdisciplinary research programme developing computational models to understand molecular interactions relevant to antimicrobial