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excellent customer service and quality services for our staff, students and researchers. Find out more about our organisation and values: The team The Network team sits within the core area of Engineering in
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services for our staff, students and researchers. Find out more about our organisation and values: The team The Network team sits within the core area of Engineering in IT Services, providing underpinning
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undertake research in the Autonomy and Verification Network. CRADLE https://cradlerobotics.co.uk Centre for Robotics and AI https://www.robotics.manchester.ac.uk Amentum https://www.amentum.com UK Research
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semantic mismatch between the formal guarantees produced by neural network verification tools and the actual implementation of neural networks at the hardware level. Specifically, hardware-level effects
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power systems – specifically voltage sourced converter based HVDC. The work will involve developing and analysing models of VSC-HVDC, their associated controls, and network connection, in PSCAD and Matlab
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troubleshooting. A good working knowledge of virtualisation and container technologies. A good working knowledge of networking, network storage protocols (e.g. NFS, SMB) and platform security principles. Good
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technologies. A basic understanding of networking, network storage protocols (e.g. NFS, SMB) and platform security principles. Basic scripting and automation skills (e.g. Python, Bash, Perl). A keen appetite to
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the UK power network. This National Grid funded post aims to develop new QB coordinated control strategies to manage future network constraints and optimise effective power flow control. The successful
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networks involved in controlling the switch between monomeric secretion and fibril formation, and characterise an endogenously-tagged collagen mouse model developed within the lab. The project will employ a
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neural networks for multimodal image analysis will be essential, as well as expertise in Python and C/C++ for scientific computing, and in ML/DL frameworks like TensorFlow, PyTorch, Keras, and Scikit-learn