About the Project
Two fully funded 4-year PhD positions are available in the Cox Group at Durham University, funded by the Royal Society. The positions start in October 2027 and are open to applicants who qualify for UK ("home") fees. International students should contact [contact details available in the full listing] to discuss other potential funding options.
The research
Aided by machine learning, the Cox Group has recently made significant advances in classical density functional theory (neural cDFT) for complex fluids relevant to chemistry. Recent highlights:
- Phys. Rev. Lett. 134, [contact details available in the full listing]): accurate incorporation of electrostatics into neural cDFT.
- J. Phys.: Condens. Matter 37, [contact details available in the full listing]): a rigorous theory of electromechanics in fluids.
- Nat. Commun. 17, [contact details available in the full listing]): discovery of dielectrocapillarity, a new phenomenon in which electric field gradients influence the adsorption of polar fluids in porous media.
- J. Phys. Chem. B 130, [contact details available in the full listing]): the thermodynamic driving forces governing selective adsorption of chemical mixtures in pores.
- Proc. Natl. Acad. Sci. 123, e[contact details available in the full listing]): coupling neural cDFT with machine-learned interatomic potentials to understand the statistical mechanics of liquids from first principles.
Building on these foundations, we now aim to uncover the thermodynamic principles underlying chemical separation and electromechanical behaviour (i.e., how matter distributes under electric fields) at the nanoscale. This supports the long-term goal of establishing design principles for improved nanoscale devices.
The projects
- Project 1 will focus primarily on chemical separation, complementing our existing research on carbon dioxide mixtures.
- Project 2 will focus primarily on electromechanics, complementing our current efforts to model electrochemical interfaces.
Depending on the candidate's interests and expertise, both projects offer scope to explore nonequilibrium effects. Both will develop theoretical and computational skills, including machine learning and molecular simulation.
Who should apply
We strongly encourage applications from students with a solid background in statistical mechanics, whether in Chemistry, Physics, Materials Science, or a related discipline. Some experience with scientific programming (e.g., Python) is helpful but not essential.
How to apply
To apply, or for informal enquiries, email [contact details available in the full listing] with:
- A brief personal statement outlining your research experience, skills relevant to the project(s), and your goals for a PhD.
- A CV including your performance in relevant university courses.
- Names and email addresses of two academic or professional referees.
Funding Notes
Funded by the Royal Society. Stipend and UK fees funded for four years.
