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Mixing of cohesive and wet materials

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Manchester, United Kingdom

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Mixing of cohesive and wet materials

About the Project

This PhD project focuses on the simulation and modelling of wet and cohesive powders, which play a crucial role in industries such as pharmaceuticals, food processing, and additive manufacturing. Despite their importance, the complex behaviour of these materials — driven by capillary forces, adhesion, and particle cohesion — remains challenging to predict and control.

The research will begin with detailed particle-based simulations to capture the micro-mechanical interactions in wet and cohesive systems, using open-source software such as MercuryDPM. These high-fidelity simulations will provide the foundation for understanding key phenomena like agglomeration, flow behaviour, and mixing efficiency.

Building on these insights, the project will then develop reduced-order models (ROMs) to describe the system’s behaviour in a more computationally efficient manner, using tools like pyMOR. These ROMs will balance accuracy and speed, enabling faster predictions for large-scale processes. Finally, the ROM will be solved in oomph-lib and advanced optimisation techniques will be applied to design and improve the quality of mixing.

This work offers the opportunity to combine fundamental physics, numerical modelling, and data-driven approaches to tackle industrially relevant challenges. It would suit a candidate with a strong background in applied mathematics, physics, or engineering and an interest in computational modelling and optimisation and industrial application.

Eligibility

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.

Funding

This 3.5-year PhD project is fully funded and home students are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.

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

Before you apply

We strongly recommend that you contact the supervisor for this project before you apply. 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.

How to apply

Apply online through our website: https://uom.link/pgr-apply-2425

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

  • Final Transcript and certificates of all awarded university level qualifications
  • Interim Transcript of any university level qualifications in progress
  • CV
  • Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
  • Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
  • English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.

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