Join the Cox Group

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, 148001 (2025): accurate incorporation of electrostatics into neural cDFT.

• J. Phys.: Condens. Matter 37, 285101 (2025): a rigorous theory of electromechanics in fluids.

• Nat. Commun. 17, 2661 (2026): 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, 4455 (2026): the thermodynamic driving forces governing selective adsorption of chemical mixtures in pores.

• Proc. Natl. Acad. Sci. 123, e2610049123 (2026): 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.

• 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.

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.

To apply, or for informal enquiries, email Stephen Cox 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.