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Field
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We are seeking a motivated and skilled Research Fellow to join the AUT Regenerative Environments Network (RegEN), based within the School of Science. This role offers an exciting opportunity
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-depth characterisation and annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based
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of the Information Modelling Framework (IMF: https://www.imfid.org) - a logic-based, scalable framework for representing structural specifications and domain knowledge across complex engineering and industrial systems
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applications would be particularly beneficial, although not essential. In addition, experience in artificial intelligence, cloud computing, sensor networks and/or data visualisation would also be highly
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ongoing development of communication updates for web, social media and networking Promote equality and value diversity, acting as a role model and fostering an inclusive working culture Support the work
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of training. Excellent organisational skills. Must display clarity of thinking and ability to address a variety of research topics. Ability to communicate complex information clearly. Ability to build
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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Scalable Quantum Devices for the Future Quantum Internet. We seek an outstanding experimental physicist to join the Hybrid Quantum Networks Lab (HQNL ) as part of the UK’s Integrated Quantum
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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and
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Fixed Term until 31/03/2026 The University of Southampton is committed to excellence in all we do, applying our insights and inventiveness to solve complex engineering and environmental challenges