Research Associate in Theoretical and Computational Condensed Matter Physics

We invite applications for a postdoctoral research associate position in theoretical and computational condensed matter physics. The successful candidate will conduct research on correlated and topological systems using state-of-the-art machine learning and electronic structure techniques. The primary focus of the project is to design and implement machine learning pipelines to efficiently investigate electron and spin systems. The position offers close collaboration with both theoretical and experimental groups within CMPMSD.

This appointment is for two years, with the possibility of renewal for a third year depending on funding availability and research performance.

Essential Duties and Responsibilities:

• Perform research on correlated electron and spin systems using ab-initio, model Hamiltonian and machine learning-based methods.
• Publish high-quality papers and give external presentations on your work.
• Collaborate actively with various theoretical and experimental groups within CMPMSD.

Required Knowledge, Skills, and Abilities:

• Ph.D. in Theoretical Condensed Matter Physics, Chemistry, Materials Science, or related discipline.
• Demonstrated expertise in machine learning techniques and proficiency in scientific programming (Python and/or Fortran/C++).
• Strong publication record in peer-reviewed journals and presentations at professional conferences, or equivalent public research contributions (e.g., GitHub repositories).

Preferred Knowledge, Skills, and Abilities:

• Knowledge of Density Functional Theory calculations and Wannier method-based spin Hamiltonian downfolding techniques.
• Familiarity with high throughput computing using modern GPU hardware as well as other computational methods such as Monte-Carlo and mean-field calculations.