Weak sequential stability of solutions to a nonisothermal kinetic model for incompressible dilute polymeric fluids
Bulicek, M
Malek, J
Suli, E
Mathematical Models and Methods in Applied Sciences
(02 Apr 2026)
The four-day free careers conference for Oxford University DPhils and Research Staff, Careers Beyond Academia: Options and Pathways for Researchers, returns in 2026. Join online panel discussions featuring PhD holders and former academic researchers now working across a range of industries, and attend in-person events exploring the current job market. The conference concludes with a careers fair, offering opportunities to connect with employers actively seeking to recruit researchers.
December:
There was one but they have left.
January:
Mehrdad Kalantar, Senior Research Fellow, Functional Analysis: S1.11
Eric Pitchon-Pharabod, PDRA in Mathematical Physics: S1.52
Fanny Bergstom, PDRA in Infectious Disease Modelling, Mathematical Biology: S4.04
Ana Djurdjevac, Associate Professor in Numerical Analysis: S1.12
Benjamin Walker, PDRA in Rough Path Theory for Applications, Mathematical and Computational Finance: S1.47
William Perkin C of E High School, a state school in West London, are running an Oxbridge Preparation Day for the Y12 students and are looking for a Maths subject specialist to come to the school on Monday 29th June for half a day to work with their students.
They will pay an honorarium of £200 plus travel expenses from Oxford. Contact Veronica Davies (@email).
Linear response and exact hydrodynamic projections in Lindblad equations with decoupled Bogoliubov hierarchies
Essler, F
Penc, P
SciPost Physics
Problems on handlebody groups
Andrew, N
Hensel, S
Hughes, S
Wade, R
Royal Society Open Science
volume 13
issue 2
(18 Feb 2026)
Thu, 18 Jun 2026
12:00 -
13:00
L3
TITLE TBC
Alexandra Tzella
(University of Birmingham)
The join button will be shown 30 minutes before the seminar starts.
Thu, 11 Jun 2026
12:00 -
13:00
L3
Koopman Spectra from Data: Guarantees, Limitations, and Implications for Prediction
Matthew Colbrook
(DAMTP University of Cambridge)
Abstract
A central challenge in applied mathematics is to extract predictive structure from data generated by complex dynamical systems. Koopman operator methods provide a principled framework for this task by embedding nonlinear dynamics into a linear operator acting on observables, reducing analysis and forecasting to questions about spectral approximation.
In this talk, I will present recent results on the analysis of data-driven Koopman methods, with an emphasis on when spectral quantities can be reliably approximated from finite data. I will describe a general framework that connects operator-theoretic properties of the Koopman operator with the behaviour of practical algorithms, clarifying phenomena such as spectral pollution and the role of continuous spectra. I will also discuss fundamental limitations: there exist classes of dynamical systems for which finite data cannot recover meaningful spectral information, placing intrinsic constraints on what Koopman-based approaches can achieve. Building on this, I will show how spectral approximation errors translate into quantitative bounds for forecasting, capturing how approximation and statistical errors propagate over time and ultimately limit long-term prediction. These results have implications for applications including fluid dynamics, molecular systems, and geophysical flows. I will conclude by highlighting open problems at the intersection of operator theory, numerical analysis, and scientific machine learning.