Professor Matthew Juniper



Professor of Thermofluid Dynamics
Fellow of Trinity College
ORCID 0000-0002-8742-9541
Scopus 6602882661

2024 Fellow of the Alan Turing Institute
2023 Commissioner, Institute for Government
2021 Fellow of the American Physical Society
2018 Associate Editor, J. Fluid Mech.
2016 PI UK Fluids Network
2015 Professor of Thermofluid Dynamics
2012 Reader in Mechanical Engineering
2006 Fellow of Trinity College
2003 Lecturer, University of Cambridge
2001 Associate, McKinsey & Co.
2001 PhD, Ecole Centrale de Paris
1997 MEng, University of Cambridge

Research Summary

My group and I model flows, investigate their physics, and optimize their behaviour.

We usually start from physics-based models of a flow inside or around a device. We infer the models' parameters from experimental data using Bayesian Inference accelerated with adjoint methods. This turns qualitatively-accurate models into quantitatively-accurate models and ranks different models by their evidence (their marginal likelihood), given the data.

We then sometimes set targets and constraints and, with adjoint methods, determine how the targets are affected by all the model parameters. We then use gradient-based algorithms to optimize the flow through or around the device. If we do not know the physics, or cannot model it, then we use data-driven approaches.

Click on the links above to find out more and see the jobs page for available PhD and PDRA projects with the group.