Talent recognised by fellowship success

Congratulations to Chancellor’s Fellows Drs Gurtej Kanwar, Sarah Rugheimer and Catriona Wimberley.

Chancellor’s Fellows

The University of Edinburgh is committed to supporting talented early career researchers through the recruitment of Chancellor’s Fellows: a prestigious 5-year tenure track fellowship scheme focused on innovative research.

The Fellows recruited in this round will complement and extend research and innovation within the University and forge new areas of focus which may involve leading a major area of research, forging new industry partnerships, or driving initiatives to strengthen research-led teaching innovations.

The scheme builds in a focus on research and innovation in the first few years, and over time, Fellows will take up the full range of core academic activities, including teaching and academic leadership. 

Dr Gurtej Kanwar

Dr Kanwar’s research focuses on the development of generative Artificial Intelligence methods and the advancement of fundamental physics with these tools. He is particularly interested in generative models capable of precisely sampling fluctuations of the quantum fields believed to describe all matter in our universe. These methods can overcome roadblocks in numerically challenging calculations necessary to better understand particle physics and to discover new physics. He will join the School in autumn 2024.

Dr Sarah Rugheimer

Dr Rugheimer is an astrophysicist working on the habitability of Earth-like exoplanets. In her research she models the climate and photochemistry of early Earth and Earth-like planets to better understand habitability and how we could detect and characterise habitable worlds with future telescopes like the Habitable Worlds Observatory and LIFE – the Large Interferometer for Exoplanets. She will join the School in 2025.

Dr Catriona Wimberley

Dr Wimberley works in medical imaging physics and her fellowship will focus on the development of novel methods for the quantification of Positron Emission Tomography (PET) imaging data, a functional, molecular imaging modality used in the study of diseases such as cancer and dementia. She will harness the most recent technological break throughs such as multi-modal imaging (PET and magnetic resonance) and Total Body PET and develop methods to extract more disease-relevant information from medical imaging data.