Condensed Matter and Quantum Information Physicist for AI Training

Apply advanced condensed matter and quantum information expertise to help train next-generation AI systems through a frontier research benchmark focused on the PXP model, Rydberg blockade, quantum many-body scars, and constrained dynamics. Prior AI experience is not required.

• Develop and analyze exact diagonalization implementations using translation and reflection symmetries, preferably with QuSpin, for system sizes of L ≥ 26.
• Construct and manipulate block-diagonalized subspaces for complex quantum systems while optimizing computational efficiency and fidelity.
• Evaluate overlaps with Z2 states and interpret the physical implications for constrained quantum dynamics.
• Provide detailed written feedback, analytical reports, and benchmark assessments related to quantum many-body scars and Rydberg blockade phenomena.
• Contribute as a Solver, Auditor, or Adjudicator based on subfield alignment, hands-on methods expertise, and seniority.

• Advanced academic background, PhD or equivalent practical experience, in condensed matter physics, quantum information, or a closely related field.
• Demonstrated expertise in exact diagonalization, including translation and reflection symmetries for large system sizes.
• Experience with QuSpin is preferred.
• Proven ability to block-diagonalize Hamiltonians and work with large Hilbert-space subspaces.
• Strong knowledge of quantum many-body scars, Rydberg blockade, and constrained quantum dynamics.
• Experience analyzing overlaps with Z2 or related quantum states and interpreting numerical results.

• Remote contract engagement.

$100 to $170 per hour.

Apply through the available application flow using email or a Google account, with agreement to the applicable terms and privacy policies.