Postdoctoral Research Associate - High-Throughput Discovery of Correlated High-Entropy and Topological Materials

Location: Denton, TX | Full-time (1.0 FTE), one-year appointment with flexible start date | Security-sensitive position | ORP retirement eligible

About the Role

We seek a postdoctoral researcher for an NSF-funded Designing Materials to Revolutionize and Engineer our Future (DMREF) collaboration. The project builds a closed theory–experiment loop for the discovery of correlated high-entropy materials and their quantum properties: high-throughput computation proposes candidate systems, experimental partners synthesize and characterize them, and the results drive more accurate calculations and a wider range of computed properties. Data will flow through a shared repository, updated in real time and released publicly at the end of the project (with the goal of integration into databases such as AFLOW).

The postdoc will extend PAOFLOW, our software suite to build tight-binding Hamiltonians from projections of DFT wavefunctions on atomic orbitals and compute a plethora of properties, among which: Berry-curvature properties, transport, optical response, and topological invariants. A central goal is to move beyond ideal crystals to realistic, compositionally disordered materials. We will do this by assembling large supercell Hamiltonians from small “graph prototype” cells in the local-orbital basis, which extends the reach of ab initio accuracy to nm–μm length scales.

Responsibilities

• Develop and implement methods for disordered and high-entropy systems in the PAOFLOW framework, including Hamiltonian construction from graph prototypes and validation against full DFT.
• Run and automate high-throughput DFT+U+V (ACBN0/eACBN0) screening on leading HPC systems.
• Compute electronic, optical, anomalous and spin transport, and topological properties (Berry curvature, anomalous/spin Hall conductivity, spin texture, quantum oscillations, superconductivity, etc.), and compare them directly with experimental data.
• Quantify errors and uncertainty in theoretical predictions and design remediation strategies, so that calculations can guide synthesis decisions.
• Maintain the data repository and feedback workflow with experimental collaborators; publish, present, release open-source software, and help mentor students.

Requirements

• Ph.D. in physics, materials science, chemistry, or a related field (conferred by the start date).
• Demonstrated experience with plane-wave DFT codes (Quantum ESPRESSO is preferred) and the electronic structure of solids.
• Strong Python skills and experience with scientific software development and large-scale calculations on HPC clusters.
• A strong publication record and the ability to work in an interdisciplinary team with experimentalists.

Preferred Qualifications

• Experience with tight-binding or Wannier-type methods, topological materials, or Berry-curvature-derived properties.
• Experience with high-throughput workflows, materials databases, or machine learning for materials.
• Familiarity with GPU/parallel Python or with modeling disorder and alloys.