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OpenTrain AI, Inc.

Condensed Matter Magnetism Physics Expert

northern, KY

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Job description

The work

You will provide scientific analysis for AI training work involving magnetic structures, neutron scattering, magnetic space groups, and the physical signatures of magnetic order. Prior AI training experience is not required.

The work uses theoretical and experimental data to assess magnetic phases and connect optical and scattering observations to their underlying physical mechanisms.

Analyze magnetic structure factors using propagation-vector methods and advanced symmetry concepts.

Identify antiferromagnetic, ferromagnetic, and related magnetic phases from theoretical and experimental data, including neutron diffraction results.

Curate and validate magnetic space group assignments using MAGNDATA and BNS notation.

Evaluate birefringence and magneto-optic Kerr effect signatures for selected materials.

What it pays and takes

This is a remote, part-time contractor role. The project is expected to last approximately 8 to 10 weeks, with about 10 hours of work per week.

The role is open to applicants in the countries listed for the opportunity and requires English-language work.

Pay: $80 to $160 per hour.

Time: About 10 hours per week for approximately 8 to 10 weeks.

Work arrangement: Remote contractor role.

Experience: Advanced academic training or equivalent research experience in condensed matter physics, with a focus on magnetism or a related field.

Required knowledge: Magnetic structure factor calculations, propagation-vector analysis, symmetry-based phase identification, and neutron scattering.

Required experience: Classifying magnetic space groups and using MAGNDATA and BNS notation.

You must be able to connect birefringence and magneto-optic Kerr effect responses to underlying physical mechanisms.

About AI training work

OpenTrain AI is the hiring and contracting organization for this role. AI training work is the human work behind systems that learn from examples, including reviewing and evaluating technical information so models can improve their reasoning.

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Who can apply

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