"PhD Studentship: Optimization of Multiscale Predictive Models for Surfactant Self-Assembly"

The University of Manchester

Qualification Type:PhDLocation:ManchesterFunding for:UK StudentsFunding amount:£20,780 annual tax-free stipend set at the UKRI rate (2025/26) and tuition fees will be paid.Hours:Full TimePlaced On:16th January 2026Closes:16th April 2026

Application deadline: All year round

Research theme: Chemical engineering, Applied physics

UK only

This 3.5 year PhD project is fully funded and home students, and EU students with settled status, are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to increase each year.

We recommend that you apply early as the advert may be removed before the deadline.

This fully funded PhD position, offers an exciting opportunity to develop and optimize multiscale models for surfactant behaviour, bridging the gap between molecular simulations and rheology. This interdisciplinary project will provide valuable insights into micellar dynamics, surfactant self-assembly, and nonlinear rheology, with direct industrial relevance to consumer product formulation and process optimization.

This project aims to enhance the accuracy of surfactant behaviour predictions by integrating molecular dynamics simulations with rheological models, focusing on model surfactant systems. The successful candidate will have the opportunity to work closely with an industry partner gaining access to cutting-edge research facilities, and real-world datasets.

The successful candidate will work under the supervision of Dr. Steph Flores (steph.flores@manchester.ac.uk) and co-supervised with Dr. Carlos Avendaño (carlos.avendano@manchester.ac.uk) and Prof. Christopher Hardacre (c.hardacre@manchester.ac.uk) at the University of Manchester, benefiting from a dynamic academic environment and strong industry collaboration. This interdisciplinary PhD is ideal for candidates with a background in chemical engineering, physics, soft matter science, computational chemistry, and/or related disciplines.

Early applications are encouraged, as the position may be filled before the deadline.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

To apply, please contact the main supervisor, Dr Steph Flores - steph.flores@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.