PhD Studentship: Coupling Material Simulation and Machine Learning to Speedup Polymer Compounds Development

To apply, please click the 'Apply' button, above.

The fully funded project covers home fees and a stipend set at the UKRI rate (£18,622 for 23/24).

A fully funded PhD position in multiscale modelling of polymer composites is available in the group of Professor Carbone at the University of Manchester from September 2024.

One of the biggest problems in the formulation of polymeric materials is the choice of the small diluent molecules, known as plasticizers, routinely added to the polymer matrix to improve its compatibility of the fillers and reduce the compounds glass transition temperature. The apparently erratic effect that the chemical composition of the plasticisers has on the properties of the final composite make the choice of which one to use a trial-and-error exercise.

There are presently around 100 plasticisers manufactured of commercial importance. In this project we will develop a computational procedure to screen thousands of different potential plasticizer molecules to identify the best molecules for the filler/polymer compound of interest. The work will include the development of workflow for high throughput simulations of the polymer and plasticizers for model reduction and of the machine learning models to identify the best candidate molecules.

The project will involve the use of molecular dynamics and machine learning methods and it is co-sponsored by Continental. The student will have the opportunity to work closely with the scientists in Continental where they will also spend a 3-months secondment during their study.

Informal enquiries can be sent along with a CV to [email protected]

Eligibility criteria: the fellowship is available for home students only, although international students with a strong CV in simulations are also welcome to send informal enquires.

To be considered home student you must meet one of these criteria:

• be a UK national (meeting residency requirements)
• have settled status
• have pre-settled status (meeting residency requirements)
• have indefinite leave to remain or enter.