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’ integration with silicon nitride photonic structures” (NANORETINA) project you will develop optoelectronic arrays, based on advanced photoactive materials, such as organic, perovskites and two-dimensional
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optimisation before physical manufacturing begins. This project aims to develop advanced deep learning models capable of predicting fabrication outcomes and guiding fabrication recipe optimisation. By learning
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EPSRC-funded “Retinomorphic nanoscale synapses’ integration with silicon nitride photonic structures” (NANORETINA) project you will develop optoelectronic arrays, based on advanced photoactive materials
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. This position focuses on the development of innovative microfluidic-based sensors for continuous, in-situ chemical and biochemical analysis. About the Role: The successful candidate will join a dynamic research
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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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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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Si photonics and material integration technology, while Southampton can provide the state-of-the-art MEMS, NEMS, and Metasurface technologies. Starting from the development of individual MEMS/NEMS
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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 efficiency, durability, and ease
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. Starting from the development of individual MEMS/NEMS-integrated photonics components, novel photonic integrated circuits (PICs) for secure communication will be demonstrated. The laboratory-level
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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