Remote Nuclear-Physics Problem Designer (Contract)

Mercor; Lacaussade; Sur place; EUR 83 000 - 119 000; Temps partiel

Résumé du poste

Mercor is seeking a Computational Particle & Nuclear Physics Expert to design graduate-level problems for AI systems, using real scientific software and Python in a remote, part-time capacity. The role demands 15–20 hours per week and strong background in physics and software tools.

You will work in Linux/terminal environments, validate problem setups, and test against advanced AI models. Collaboration with cross-disciplinary teams will be asynchronous to improve model performance.

Qualifications

• Graduate-level training in a relevant STEM field (MS/PhD, or equivalent).
• Proven proficiency with scientific software libraries, demonstrated through research publications or professional work.
• Strong Python skills for writing problem setups and solution validators.
• Ability to work in a Linux/terminal environment with remote compute sandboxes.
• Available for 15–20 hours/week.

Responsabilités

• Design graduate-level computational problems for AI to solve using real scientific software.
• Develop problems requiring strategic planning and multi-step workflows in Particle & Nuclear Physics.
• Test and refine problems against state-of-the-art AI models to achieve target difficulty.
• Utilize scikit-hep and other HEP Python tools for data analysis and simulations.
• Work independently to refine problem designs based on feedback.
• Collaborate asynchronously with teams to improve AI model performance.

Connaissances

Python

Linux/Unix

Scientific libraries

Research experience

Formation

MS/PhD in STEM

Outils

scikit-hep

Containerized environments

Description du poste

Mercor is seeking a Computational Particle & Nuclear Physics Expert to design graduate-level problems for AI systems, using real scientific software and Python in a remote, part-time capacity. The role demands 15–20 hours per week and strong background in physics and software tools.

You will work in Linux/terminal environments, validate problem setups, and test against advanced AI models. Collaboration with cross-disciplinary teams will be asynchronous to improve model performance.