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, to develop a novel end-to-end neuromorphic design approach based on spiking neural networks (SNNs). The project aims to develop novel computing solutions for the defence and security sector, that can
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, to develop a novel end-to-end neuromorphic design approach based on spiking neural networks (SNNs). The project aims to develop novel computing solutions for the defence and security sector, that can
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to addresses the main priorities of the engineering net zero mission by accelerating deployment of a new technology that unlocks net-zero chemicals/e- fuels production and offers a route to valorise the CO2
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cellular defects relating to cilia motility, prior to high throughput testing of novel therapies. Through the national LifeArc collaboration you will have opportunities to network and work with scientists
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(quantitative/network meta-analysis) Medical statistics (survival analysis) HTA methods and processes * Applications will be considered from candidates who are working towards or nearing completion of a relevant
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for collaboration and publication. The role will be responsible for supporting quantitative data analysis alongside community researchers; networking with food suppliers and aid providers to develop a novel online
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networks or other machine learning models. For this position we are targeting a salary corresponding to Level 4 Spine Point 28 - 30. Actual compensation offered will be determined by factors such as job
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interdisciplinary research crossing hardware, software, networking, and AI domains Excellent ability to present complex technical concepts clearly in both written form and orally Applications for the position will be
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welcome and should be directed to Dr Fatima Pereira: f.c.pereira@soton.ac.uk . What we can offer you A world-class research environment Access to mentoring, career development and networking across
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simulation tools for loads calculations; Experience with developing, training, and optimizing neural networks or other machine learning models. For this position we are targeting a salary corresponding to