DPhil in Soft Matter: Mesoscale Polymer Network Modelling

University of Oxford; Oxford; On-site; GBP 20,000 - 24,000; Full time

Benefits offered by this job

Fees covered

Stipend £21,805 p.a.

Job summary

University of Oxford invites applications for a 3.5-year D.Phil. studentship in the Brassart Group, focusing on mesoscale modelling of polymer networks to predict time-dependent deformation and failure.

The project combines physics-based modelling with experimental validation and molecular simulations where appropriate. The successful candidate will develop physically grounded viscoelastic and chain-scission models, analyse network topology, and apply statistical mechanics and stochastic

Qualifications

• First class or strong upper second-class undergraduate degree with honours in Engineering, Physics, Mathematics, Materials, or Chemistry.
• Excellent English written and spoken communication skills.
• Strong analytical and mathematical skills.
• Programming experience (e.g. Matlab, Python, C/C++,...)

Responsibilities

• Develop a mesoscale modelling framework to predict time-dependent deformation and failure of polymer networks.
• Use a coarse-grained discrete-network model to represent network architecture.
• Test predictions against existing experimental data and simulations where appropriate.

Skills

Analytical skills

Computational skills

Programming experience

Education

First class or strong upper second-class undergraduate degree in Engineering, Physics, Mathematics, Materials or Chemistry

Tools

Matlab

Python

C/C++

Job description

University of Oxford invites applications for a 3.5-year D.Phil. studentship in the Brassart Group, focusing on mesoscale modelling of polymer networks to predict time-dependent deformation and failure.

The project combines physics-based modelling with experimental validation and molecular simulations where appropriate. The successful candidate will develop physically grounded viscoelastic and chain-scission models, analyse network topology, and apply statistical mechanics and stochastic