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About the Role
Develop new theoretical and computational approaches to calculate intrinsic magnetic properties of materials by deriving and implementing formalisms that go beyond standard DFT treatments, utilizing Green's function and many-body methods. This role is for someone excited about pushing the boundaries of DFT and making a meaningful contribution to material science.
Responsibilities
- Develop simplified, physically transparent methods for predicting main magnetic properties from first principles using Feynman diagrams, path integrals and other analytical and computational tools of contemporary quantum many-body theory.
- Apply GW / DMFT, or other Green's function functionals where DFT self-interaction errors are significant, particularly for strongly-correlated d and f systems.
- Collaborate with the DFT team to implement new formalisms in production workflows.
- Collaborate with the ML team to use improved predictions as training targets.
- Maintain awareness of the broader literature on magnetism, spintronics, strong correlations, development of density and Green's function functionals and bring new ideas into the research programme.
Requirements
- PhD in theoretical or computational condensed matter physics.
- Strong background in DFT and/or other first-principles methods.
- Experience with Green’s function-based many-body methods: GW approximation, DMFT, or equivalent diagrammatic techniques.
- Demonstrated ability to derive physical expressions analytically and implement them numerically.
- Proficiency in Python and at least one compiled language (Fortran, C/C++, or Julia).
- Evidence of significant research impact through publications on DFT, atomistic modelling, computational materials science, or related technical disciplines.
Skills
- DFT
- Quantum many-body physics
- Green's function methods
- Many-body methods
- Feynman diagrams
- Path integrals
- GW approximation
- DMFT
- Python
- Fortran
- C/C++
- Julia
- Wannier functions
- Tight-binding downfolding
- Wannier90
- VASP
- Quantum Espresso
- GPAW
Location
- London-based
Work Type
- Flexible approach to how and where you work
Experience Level
- PhD
Education Level
- PhD
Salary/Compensations
- Competitive salary
Benefits
- Generous equity
- Benefits
About the Company
- Diffractive is building the AI Material Scientist that autonomously learns from real-world experimentation to push the boundaries of scientific discovery.
- We're early, moving fast, and working on problems that genuinely matter.
- You'll join a small, high-calibre team where your work has real impact from day one.
Equal Opportunity
- Diffractive is an equal opportunities employer.
- We are committed to creating an inclusive environment for all employees and welcome applications from people of all backgrounds, experiences, and identities.
- If you require any adjustments or accommodations at any point during the interview process please let us know - we will be happy to help.