Professor Richard Clayton

Professor of Computational Physiology
Deputy Head of Department

Telephone: +44 (0) 114 222 1845

Member of the Complex Systems Modelling research group
Personal website:

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Prof. Richard Clayton



Richard Clayton has a first degree in Applied Physics and Electronics from the University of Durham, and a PhD in Medical Physics from the University in Newcastle upon Tyne. After completing his PhD, Richard continued research at the Freeman Hospital in Newcastle upon Tyne, funded by British Heart Foundation junior and intermediate fellowships. The main focus of these projects was on understanding the electrical mechanisms that underlie life-threatening disorders of heart rhythm, based on the analysis of data recorded from patients. A move to the University of Leeds, funded by a British Heart Foundation Lectureship enabled Richard to work on developing mechanistic models of cardiac electrophysiology in the human heart. Richard Clayton was appointed Senior Lecturer in the Department of Computer Science in Sheffield in 2003, promoted to Reader in Computer Science in 2008, and Professor of Computational Physiology in 2014. He is a core member of the INSIGNEO institute for in-silico Medicine, and serves on the INSIGNEO board.

Other Professional Activities and Achievements

  • Member of INSIGNEO board
  • Member of EPSRC review college


Richard Clayton’s research interests are focussed on developing physics-based and mechanistic computational models and simulations as tools to examine the structure and function of human tissues and organs. This theme aligns with the recently established INSIGNEO institute for in-silico medicine in Sheffield. Richard Clayton has had a long running interest in developing computational models to investigate the mechanisms that initiate and sustain dangerous disorders of heart rhythm in the human heart, and this has been funded through grants and fellowships from the British Heart Foundation. Recent funding from EPSRC will develop techniques to handle the quantification and propagation of uncertainty within multiscale models of physiology.


Current grants

Previous grants