About this role
Role Overview
Apply advanced condensed matter and magnetic-materials expertise to produce high-quality domain input that helps train next-generation AI systems. This remote contract role focuses on magnetic space groups, neutron scattering, BNS notation, and the physical signatures of magnetic order. Prior AI experience is not required.
Key Responsibilities
- Analyze and interpret magnetic structure factors using propagation-vector methods and advanced symmetry concepts.
- Identify and classify magnetic phases, including antiferromagnetic, ferromagnetic, and related phases, using theoretical and experimental data such as neutron diffraction results.
- Curate and validate magnetic space group assignments against established datasets, including MAGNDATA, and correlate findings with BNS notation conventions.
- Evaluate birefringence and magneto-optic Kerr effect signatures in selected materials, linking optical phenomena to their underlying magnetic structures.
- Apply subject-matter expertise to help improve how AI systems learn, reason, and perform.
Qualifications
- Advanced academic background in condensed matter physics, with a specialization in magnetism or a related field, through a PhD or equivalent research experience.
- Proficiency in magnetic structure factor calculations, propagation-vector analysis, and symmetry-based phase identification.
- Direct experience classifying magnetic space groups, including hands-on familiarity with MAGNDATA and BNS notation.
- Strong knowledge of neutron scattering techniques for magnetic structure determination.
- Ability to connect experimental signatures, including birefringence and magneto-optic Kerr effect, to underlying physical mechanisms.
Work Terms
- Remote contract engagement.
Compensation
- $80 to $160 per hour.