PhD Studentship: Quantum Dynamics of Defects in Disordered Alloys

About the Project

Predicting how defects such as dislocations and grain boundaries move in complex, disordered metallic systems remains a central open problem in materials science. Conventional approaches typically rely on classical transition state or friction models that assume well defined equilibrium configurations and memoryless dissipation. These assumptions break down in chemically disordered systems such as high entropy alloys, where strong local fluctuations and highly structured vibrational spectra lead to intrinsically noisy, history dependent dynamics.

In these systems, defect motion is governed by quantum and finite temperature dissipation, long lived memory effects, and collective phonon scattering processes that cannot be captured by standard Markovian models.

The Project

This PhD project aims to develop a rigorous, predictive framework for defect mobility in disordered alloys based on non equilibrium quantum statistical mechanics. The core theoretical tool is the Keldysh Green function formalism, which naturally incorporates dissipation, fluctuations, and memory effects beyond adiabatic or classical limits.

You will work at the interface between fundamental theory and large scale computation, developing methods that connect atomistic simulations to effective continuum descriptions of defect motion in infinite disordered media.

Key research directions include:

• Theoretical development: Deriving equations of motion for defects using non equilibrium Green function techniques, with explicit treatment of phonon mediated dissipation, memory kernels, and non adiabatic effects.
• Machine learning integration: Developing physics informed machine learning approaches to extract reduced dynamical descriptions, such as memory kernels and self energies, from high dimensional atomistic simulation data.
• Multiscale numerical implementation: Building workflows that link density functional theory and classical atomistic simulations to effective medium and embedding theories suitable for disordered solids.

Candidate Profile

We are seeking a highly motivated candidate with an interest in applying fundamental physics to real materials problems.

Highly desirable:

A strong grounding in materials science or theoretical condensed matter physics. Experience or interest in Green function methods, many body theory, solid state physics, numerical linear algebra, or computational physics.

Computational skills:

Experience with Python and scientific computing is advantageous. Willingness to engage with high performance computing and data driven methods is essential. Prior machine learning experience is welcome but not required.

Why This Project?

This is not a black box modelling project. The emphasis is on understanding, deriving, and controlling the physics underlying defect dynamics in complex materials. You will gain training in advanced field theoretic methods, large scale numerical simulation, and modern data science techniques, developing a rare skill set that bridges rigorous theory, computation, and materials modelling.

The project is well suited to students aiming for careers in academic research, advanced industrial R and D, or interdisciplinary work at the boundary of physics, data science, and materials engineering. International applicants are welcome.

How to Apply

Interested candidates are encouraged to make informal enquiries before submitting a formal application. Please send your CV and a brief statement of your research interests (highlighting your experience in theoretical physics/scientific computing) to b.gurrutxagalerma.1@bham.ac.uk.

Funding Notes

This studentship covers full tuition fees (at Home or International level) and provides a tax-free maintenance stipend. The stipend is aligned with UKRI rates, currently £20,780 per annum (for the 2025/26 academic year), with annual inflationary increases.

Competitive Award: The funding is awarded as part of a competitive call. Candidates will be evaluated based on academic excellence and research potential.

International Applicants: This position is open to international applicants.

Advert information