Remote Physics AI Research Collaborator

Weekday AI; United States; Remote; USD 110.000 - 152.000; Teilzeit

Zusammenfassung

Weekday AI invites applications for Physics Research Collaborators to advance AI systems that reason across physics problems. This remote, part-time role offers $80–$110 per hour for up to 20 hours per week, engaging with a leading AI research initiative focused on scientific reasoning.

You will review physics research papers, design problems to train models, assess AI-generated solutions, and provide detailed feedback to improve model performance.

Qualifikationen

• First-author or co-first-author publications in leading journals (PRL, PRX, Nature Physics, etc.).
• PhD or postdoctoral training from a globally recognized institution.
• Recipient of prestigious fellowships or awards (e.g., NSF, Hertz, Sloan, Simons, Gates Cambridge).
• Named inventor on patents or PI on funded grants.
• IPhO medal (Gold/Silver/Bronze).
• Current or recent experience in an active research role (PhD Candidate, Postdoc, Faculty, etc.).

Aufgaben

• Review physics research papers for accuracy, originality, and significance.
• Design and review challenging physics problems for AI model training/evaluation.
• Assess AI-generated solutions: derivations, predictions, simulations, experiments, literature interpretation.
• Identify reasoning gaps that experts would recognize and provide evidence-based feedback.
• Collaborate with researchers to ensure consistency and quality across evaluation tasks.

Kenntnisse

Publications in top journals

PhD or postdoc training

Prestigious fellowships

IPhO medal

PI on funded grants

Active researcher

Ausbildung

PhD in Physics or related STEM

Jobbeschreibung

Weekday AI invites applications for Physics Research Collaborators to advance AI systems that reason across physics problems. This remote, part-time role offers $80–$110 per hour for up to 20 hours per week, engaging with a leading AI research initiative focused on scientific reasoning.

You will review physics research papers, design problems to train models, assess AI-generated solutions, and provide detailed feedback to improve model performance.