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goals of the JOINER project. The ideal candidate will have hands-on experience with Juniper and Fortinet systems, and a solid understanding of MPLS, Layer 2 VPNs, network virtualization, and cloud-based
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the MRC Centre for Neurodevelopmental Disorders. At the Centre for Developmental Neurobiology (CDN), we investigate the mechanisms governing the formation of the brain during embryonic development and in
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advanced quantum optics methods in both discrete and continuous variables with cutting-edge solid-state systems aimed at pushing the limits of fundamental research and applications such as optical computing
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Manufacturing Institute Scotland (NMIS). The AFRC is seeking to appoint an experienced Materials Knowledge Exchange (KE) Fellow to lead the additive and solid-state joining research activities in the Welding and
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Engineering, Civil Engineering, Computing, Dyson School of Design Engineering, Earth Science and Engineering, Electrical and Electronic Engineering, Materials and Mechanical Engineering. What you would be doing
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solid understanding of data science approaches including deep learning prediction and classification models. What we can offer you This role offers excellent training in computer vision and machine
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
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numerical calculation skills and mathematical modelling skills Strong skills in solid state physics and quantum mechanics Experience in theoretical modelling and experimental investigation of optical devices
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computational mechanics or multiphysics modeling, with particular interest in fracture mechanics and chemo-mechanical degradation. Knowledge of solid-state defect chemistry (advantageous). You will join a dynamic
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computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading