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Positions

Open positions

A fully-funded PhD studentship is available with our group, as part of the 2025 Department of Chemical Engineering PhD Scholarship scheme. The research will be directed towards one of the two projects provided here.

Data-driven modelling and prediction of complex systems

The overarching objective is to utilise existing state-of-the-art data-driven methodologies, and, building on recent efforts in the group to develop new ones as needed, to efficiently simulate and accurately predict complex systems. Of particular interest are families of continuous deep architectures, which have recently re-emerged as “neural ordinary differential equations (ODEs)” and physics-informed neural networks (PINNs). Specifically, neural ODEs will be utilised in the first instance to analyse the dynamics of a set of ODEs (which for a sufficiently large number of ODEs will yield a partial-differential equation (PDE)), followed by stochastic differential equations (SDEs) and ultimately SPDEs. This work will be complemented by the use of PINNs where neural networks will be trained to act as the numerical solution of ODEs/PDEs/SDEs/SPDEs.

A number of prototypical systems will be considered in order of increased complexity. These include the Lorenz system, a set of three ODEs obtained from the Rayleigh-Benard convection between two parallel plates (which in turn can viewed as a model of the atmosphere) via dimension reduction. The Lorenz system exhibits non-trivial behaviour including transition to chaos. Particular emphasis will be given to a systematic sensitivity analysis to assess the predictability of neural ODEs for the prototypical systems we will consider.

General hydrodynamics of observables

Classical density-functional theory (DFT) is a powerful theoretical framework for studying the equilibrium density distribution of complex many-body systems. The core of the framework is to bridge microscopic and macroscopic descriptions of fluids through a variational principle that minimises the system’s free energy. For dynamical settings, dynamic DFT (DDFT) extends the equilibrium framework to capture the time evolution of an order parameter, typically density. However, DDFT is often insufficient for systems far from equilibrium where fluctuations become significant, necessitating a stochastic extension: fluctuating DDFT (FDDFT).

The overarching objective here is to derive a generalised FDDFT from first principles starting from the microscopic Hamiltonian dynamics using appropriate coarse-graining techniques, such as Zwanzig’s projection operator method and the principle of maximum entropy. To showcase the effectiveness and utility of the framework, we will apply it to two prototypical fluid systems: colloidal and simple (atomic-molecular) viscous fluids. In the former case, we the new formalism should recover our previously derived FDDFT. In the latter case, we should be able to recover the Landau-Lifshitz fluctuating Navier-Stokes equations, providing fundamental derivation of this well-known phenomenological model.

You can apply here.

The deadline for the first round of applications is October 31, 2025.

We invite applications by highly talented researchers for one postdoctoral research associate position on Statistical Mechanics of Nonequilibrium Processes, starting as soon as possible. The position is part of the ERC Advanced Grant, Machine-Aided General Framework for Fluctuating Dynamic Density Functional Theory (MAGFFDDFT). To apply please visit

Research Associate in Statistical Mechanics of Nonequilibrium Processes | Jobs | Imperial College London

Please feel free to contact us with any questions about the projects. For any queries about the application process, or if you are unable to apply online, please contact Mrs Sneha Saunders (chemeng.staffing@imperial.ac.uk).

The position is now formally closed. If you would like to apply, please check this space periodically for new openings.

We invite applications by highly talented researchers for two postdoctoral research associate positions, starting as early as September 2023: one on Fluctuating Hydrodynamics of Nonequilibrium Systems, and another on Machine Learning of Nonequilibrium Processes. They are part of the ERC Advanced Grant, Machine-Aided General Framework for Fluctuating Dynamic Density Functional Theory (MAGFFDDFT). To apply please visit

Research Associate in Fluctuating Hydrodynamics of Nonequilibrium Systems | Jobs | Imperial College London

and

Research Associate in Machine Learning of Nonequilibrium Processes | Jobs | Imperial College London.

The positions are now formally closed. If you would like to apply, please check this space periodically for new openings.

Other options

Openings at both PhD and post-doctoral level arise often, details will be posted on this page. There are several other funding opportunities given below. If you would like to pursue a project funded by one of these sources and the project intersects our research interests please contact us.

External Fellowships

Internal Fellowships

Imperial College Research Fellowships including Fellowships on AI in Science supported by Schmidt Futures