STFC Innovations Partnership Scheme Fellow at the University of Southampton

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The UK is dependent on innovation for long-term economic prosperity. An invaluable resource is the science training and research in universities, but the path from this training, to research 'invention', through to innovation must be established and then sustained. Here we propose the appointment of an STFC IPS Fellow at the University of Southampton, which would enable a step-change in the exploitation of our STFC-funded research outside of academia by increasing the breadth, scope and number of researchers engaging with non-academic partners (e.g., industry, commerce, third sector). An IPS Fellow will also allow a co-ordinated approach to the our STFC-area technology transfer activity.The IPS Fellow will use their significant commercial/industrial experience to translate ideas and technology from fundamental STFC-area research to generate non-academic impact. This will include identifying and showcasing our department's STFC-funded research strengths, improving our visibility outside of academia, and demonstrating how the application of our strengths and specialist knowledge can serve the interests of non-academic sectors.A key element will be placements for STFC-funded staff and students, building successful and meaningful collaboration with partners, and providing industry with access to some of the brightest minds, cutting-edge techniques, and skilled problem solvers. In return, our researchers will gain opportunities to carry out feasibility studies or to put into practise specialised techniques and expertise of sector interest. These placements also provide access to future research collaborators/consultancy opportunities and serve as links to potential mentors to encourage and support entrepreneurship. We anticipate the Fellow would concentrate on supporting non-academic engagement in the following 'STFC areas': Our Astronomy Group, Theoretical Physics Groups and Relativistic Astrophysics group are experienced with data analysis (time series, machine learning and AI for signal extraction from big data) which is readily transferable to commercial applications. For example, the work of the Southampton Particle Theory Group (SHEP) in lattice QCD has had significant impact on the development of parallel supercomputers; the IBM BlueGene/Q series was co-designed by colleagues in the RBC-UKQCD collaboration; techniques designed for detecting new events in astronomical imaging data are now being implemented in medical science to detect and monitor skin cancer; our STFC-funded researchers in Maths are using machine learning for radar applications; our Space Environment Group has expertise in the effect of space weather, the interaction of the sun and solar wind on the Earth and other planets, with implications for communications and of importance to commerce, industry and national security.