PhD in Hybrid Modelling for Sustainable Formulation Design and Innovation

Research theme: Advanced Materials and Manufacturing

This 3.5 year PhD project is fully funded and open to home students. The successful candidate will receive an annual tax free stipend set at the UKRI arte (£19,237 for 2024/25) and tuition fees. We expect the stipend rate to increase each year. 

The Department of Chemical Engineering at the University of Manchester is offering a fully funded PhD position, co-sponsored by Unilever, offers a unique opportunity to work at the interface of academic research and industrial innovation, shaping the future of Fast-Moving Consumer Goods (FMCG) formulations.

In collaboration with Unilever, this project will develop hybrid models that combine physics-informed simulations and machine learning to predict and optimise the rheological properties of surfactant formulations. By identifying key molecular features that influence flow properties, the research will accelerate product development and drive innovation in sustainable and high-performance formulations. This partnership ensures that your research will have direct industrial relevance, with access to real-world data, cutting-edge facilities, and potential career pathways in industry.

The successful candidate will work under the joint supervision of Dr. Stephen Flores (stephen.flores@manchester.ac.uk) and Dr. Dongda Zhang (dongda.zhang@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, machine learning, and other 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 supervisors for this project; Dr Stephen Flores - stephen.flores@manchester.ac.uk and Dr Zhang - dongda.zhang@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. 

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