Two PhD studentships - Neural networks in EPR

Neural network analysis of quantum processes

This project is about training and reverse-engineering deep neural networks that process and interpret magnetic resonance data. Earlier this year, we created a family of neural networks that recovered molecular distance distributions from electron spin resonance data (https://advances.sciencemag.org/content/4/8/eaat5218). However, it is utterly unclear how the networks accomplish what is actually, without careful regularisation, an impossible mathematical operation. The aim of this project is to find out. Further details are available at (http://spindynamics.org/Vacancies.php).

Two 4-year studentships are available; they are open to UK and EU nationals, and include all applicable university fees, as well as a tax-free stipend of £15,009 per year. It is likely that the students will make a major contribution to both quantum theory and artificial intelligence by exploring, developing, and analysing neural networks that process spectroscopic data. This project is a collaboration with ETH Zurich (Prof Gunnar Jeschke) and Weizmann Institute (Prof Daniella Goldfarb), and will involve visits to both of these institutions.
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Dr Ilya Kuprov FRSC
Associate Professor of Chemical Physics
Secretary to the RSC ESR Spectroscopy Group
Associate Editor, Science Advances
Office 3041, Building 30,
School of Chemistry, FNES,
University of Southampton,
Southampton, SO17 1BJ, UK.
Tel: +44 2380 594 140
Email: i.kuprov@soton.ac.uk
Web: http://spindynamics.org