Advanced Quantum Materials Theorist

We're on a mission to harness the power of quantum computing to revolutionize industries and transform the world.

Quantum computers can provide exponential performance increases over today's most powerful supercomputers, offering unprecedented opportunities for breakthroughs in climate, energy, healthcare, materials design, and many more fields.

Our unique approach combines cutting-edge photonics with high-volume semiconductor manufacturing processes, enabling us to produce scalable and reliable quantum components.

As a member of our team, you'll contribute to building a utility-scale quantum computer and the software tools needed to unlock its potential.

This role offers a rare opportunity to join an interdisciplinary collaboration at the forefront of computational physics, materials science, machine learning, and quantum technologies.

This is a chance to bring your expertise in electronic structure theory, many-body physics, and computational methods to explore complex quantum materials and emergent phenomena.

You'll integrate conventional and emerging approaches, incorporating insights from machine learning, high-performance computing (HPC), and quantum computing to drive innovation in materials discovery and design.

This position requires a PhD in computational physics (or a closely related field) and preferably postdoctoral research experience.

We're seeking a curious, creative, and interdisciplinary thinker with a comprehensive understanding of various computational physics, chemistry, and machine learning methodologies.

The ideal candidate should be an avid reader of scientific literature, have expert-level hands-on coding experience, and possess deep expertise and skills in methodology development.

• Conduct theoretical and computational research in materials theory and simulations, advancing methodologies for modeling electronic structure, strongly correlated systems, and emergent quantum phenomena.
• Develop computational workflows that integrate established and emerging techniques, leveraging advances in HPC, machine learning, and quantum computing.
• Collaborate with quantum algorithm developers, computational physicists, and materials scientists to explore new frontiers in quantum materials modeling.
• Contribute expertise in electronic structure theory and many-body physics to research efforts focused on understanding and predicting material properties.
• Stay at the forefront of developments in computational materials science, incorporating state-of-the-art approaches into research projects.
• Serve as a technical leader in interdisciplinary collaborations, working with internal and external teams on innovative solutions in quantum materials research.
• Engage in external partnerships, scientific discussions, and technical communication to support the broader research and application of advanced computational materials methodologies.

Essential Skills and Qualifications

• Ph.D. in theoretical or computational physics or chemistry or a closely related field.
• Strong foundation in electronic structure theory, band structure calculations, and computational modeling.
• Expertise in methodology development within density functional theory (DFT).
• Proficiency in scientific programming and algorithm development, with experience in Python, Fortran, or C++.
• Familiarity with HPC environments and large-scale simulations.
• Enthusiasm for interdisciplinary collaboration, working across physics, chemistry, materials science, machine learning, and computational research teams.
• Strong peer-reviewed publication record.

PREFERRED SKILLS AND QUALIFICATIONS

• Experience with machine learning and data-driven approaches in computational modeling.
• Experience with quantum algorithms, particularly their application to materials modeling and electronic structure problems.
• Familiarity with wave function-based computational chemistry methodologies, including coupled-cluster, configuration interaction (CI), and multi-reference methods.
• Expertise in many-body and strongly correlated systems, including Fermi-Hubbard models, tensor networks, and embedding theories.
• Hands-on coding experience with Wannier function calculations.