PhD in Data-Driven Design Rules for Polymer-Surfactant Formulations Using Automated Rheo-Dialysis and Scattering University of Liverpool in United Kingdom

This PhD project at the University of Liverpool offers an exciting opportunity to develop new data-driven approaches for polymer-surfactant formulations, focusing on the structure, composition, and flow of complex soft matter systems. The research will utilize a novel rheo-dialysis platform and small-angle X-ray scattering (SAXS) to systematically investigate how environmental changes affect the mechanical and structural properties of personal care products, such as shampoos. By emulating real-world conditions, the project aims to uncover molecular design principles that govern the performance and adaptability of these formulations.

Supervised by Dr Anders Aufderhorst-Roberts and Dr William Sharratt from the Department of Materials, Design and Manufacturing Engineering, with additional industrial supervision from Unilever, the project is highly interdisciplinary. Dr Aufderhorst-Roberts leads research in experimental tools for characterising soft and biological materials, with a focus on advanced rheology techniques. Dr Sharratt brings expertise in small-angle scattering, self-assembly, and AI-augmented formulation. Industrial supervisors from Unilever will provide training in formulation chemistry and support employability, including potential secondments.

The successful candidate will join the EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry (DAMC CDT), based in the Materials Innovation Factory, the UK's largest industry-academia colocation in physical science. The CDT offers comprehensive training in robotics, digital methods, and materials design, developed in collaboration with 35 industrial partners. The studentship covers full home tuition fees, a four-year maintenance grant (starting at £21,805 for 2026-27), and a Research Training Support Grant. Limited scholarships are available to cover the fee difference for outstanding international students. Additional support is available for students with disabilities.

Applicants should have a strong background in chemical engineering, chemistry, materials science, or a related field. Experience or interest in machine learning, data-driven research, and materials characterisation is desirable. Required application materials include academic transcripts, degree certificates, passport details, English language certificates (if applicable), a personal statement, CV, and referee contact details. Applications are reviewed on a rolling basis, and the position may close early if filled. Early application is encouraged.

For informal enquiries, contact Dr Anders Aufderhorst-Roberts at [email protected]. For full application instructions, review the CDT guide and apply online, indicating 'Materials Engineering' as the subject area.