Condensed Matter Physicist for AI Model Training

Develop and analyze large-scale exact diagonalization implementations for constrained quantum many-body systems, with a focus on problems such as the PXP model, Rydberg blockade, quantum many-body scars, and constrained dynamics. Your work will produce high-fidelity numerical results and written analyses that support research-level benchmarks and help train and evaluate advanced AI systems. Key Responsibilities

• Implement and analyze exact diagonalization that exploits translation and reflection symmetries, working with system sizes L 26, QuSpin experience preferred.
• Construct and manipulate block-diagonalized subspaces of large Hilbert spaces, optimizing computational efficiency and numerical fidelity.
• Compute and interpret overlaps with Z2 and related quantum states, extracting physical insights for constrained dynamics and scarred states.
• Produce detailed written feedback, analytic reports, and benchmark assessments that document methods, results, and interpretation.
• Participate in the project as a Solver, Auditor, or Adjudicator according to your subfield alignment, hands-on methods expertise, and seniority., Role Overview Use advanced Excel, Word, and PowerPoint skills to design realistic business scenarios, create and evaluate Office Open XML documents, and provide detailed, profess…
• 1 day ago

• Advanced academic training, such as a PhD, or equivalent practical experience in condensed matter physics, quantum information, or a closely related field.
• Proven expertise with exact diagonalization techniques for large systems, including the use of translation and reflection symmetries; familiarity with QuSpin is preferred.
• Experience performing block-diagonalization of Hamiltonians and working within large Hilbert space subspaces.
• Strong knowledge of quantum many-body scars, Rydberg blockade phenomena, and constrained quantum dynamics.
• Experience analyzing overlaps with Z2 or similar quantum states and interpreting numerical outcomes.

Work Terms

• Engagement type: independent contractor, remote.
• Project scope: contribution to a research-level physics benchmark used to train and evaluate advanced AI systems. Domain expertise is the primary requirement; prior AI experience is not required.
• Assignment and role on the project (Solver, Auditor, Adjudicator) will be determined based on subfield fit, technical methods experience, and seniority., * Applicants must be able to work as independent contractors. No prior AI experience is required; demonstrated domain expertise is the key eligibility criterion.

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