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research into a functional and interactive platform for clinical and industry use. The successful candidate will be responsible for refining deep learning models, conducting usability testing, and supporting
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(e.g. Matlab / Python) Experience of or willingness to work with pre-clinical tissue models (e.g. farmed meat) and human tissue (e.g. contaminated instruments in clinical facilities). A proven academic
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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, theoretical models and data analysis. Responsibilities will also include assistance in the day-to-day maintenance of the experimental facilities, liaising with external collaborators, assisting in
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as part in a collaborative decision-making process, while taking responsibility for implementing experiments, theoretical models (where appropriate) and data analysis. Responsibilities will also
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for sample processing, based on market feedback. The work is focused on the optimisation of system design to maximise performance and ensure excellent market-fit. The post involves lab-based design, assembly
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experience. Proven technical competence in experimental science and data analysis/modelling. Strong practical understanding in at least one of the following: medical device manufacturing, sustainable materials
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collaborations. Key Duties and Responsibilities The post holder is required to; Work with colleagues and line manager to devise and perform experiments, modelling and analysis consistent with the goals
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temporal properties: ultrabroadband supercontinua, intense sub-cycle field transients, and few-femtosecond ultraviolet pulses, among many others. We combine numerical modelling with experiments to study the
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-play methods and diffusion models, or specific applications such as Very Long Baseline Interferometry and black hole imaging in astronomy, or low-field magnetic resonance imaging in medicine, is of