Expert Work
Computational Physics Expert
Status: Open
- Pay
- $40–$60/hr
- Location
- Remote
- Employment type
- Contractor
- Openings
- 10
- Posted
- 2026-05-26
About this opportunity
Job Title: Computational Physics Expert
Job Type: Contractor
Location: Remote
Job Summary: In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.
Key Responsibilities:
- Leverage deep expertise in computational physics to contribute high-quality insights to AI evaluation projects.
- Analyze, interpret, and synthesize large-scale scientific data sets and experimental results.
- Develop, review, and refine quantitative models, simulations, or workflows in relevant physics domains.
- Collaborate with interdisciplinary teams to inform and improve AI model capabilities in scientific reasoning.
- Apply advanced numerical methods and computational techniques to real-world physics challenges.
- Document findings and communicate complex concepts clearly in both written and verbal formats.
- Maintain high standards of scientific integrity and accuracy in all project deliverables.
Required Skills and Qualifications:
- PhD in physics or a closely related field, or equivalent industry/research experience.
- Expertise in computational physics, scientific computing, or simulation-heavy research is desirable.
- Proficiency with Python, C++, MATLAB, and scientific tools such as ROOT would be ideal.
- Experience working with numerical simulations, Monte Carlo methods, or HPC workflows.
- Strong analytical, quantitative, and mathematical reasoning abilities.
- Demonstrated experience in experimental or scientific data analysis.
- Excellent written and verbal communication skills; ability to convey complex topics to diverse audiences.
Preferred Qualifications:
- Background in particle physics, astrophysics, plasma/fusion, or quantum systems.
- Experience with AI/ML workflows, scientific benchmarking, or evaluation practices.
- Track record of published research or contributions to scientific literature.
Required skills
- computational physics
- scientific computing
- python
- c++
- matlab
- root
- numerical simulations
- monte carlo methods
- hpc workflows
- data analysis
- quantitative modeling
- scientific data interpretation
- analytical reasoning
- mathematical reasoning
- scientific communication
- written communication
- verbal communication
- team collaboration
- scientific integrity
- ai/ml workflows
- scientific benchmarking
What you'll do
- Leverage deep expertise in computational physics to contribute high-quality insights to AI evaluation projects.
- Analyze, interpret, and synthesize large-scale scientific data sets and experimental results.
- Develop, review, and refine quantitative models, simulations, or workflows in relevant physics domains.
- Collaborate with interdisciplinary teams to inform and improve AI model capabilities in scientific reasoning.
- Apply advanced numerical methods and computational techniques to real-world physics challenges.
- Document findings and communicate complex concepts clearly in both written and verbal formats.
- Maintain high standards of scientific integrity and accuracy in all project deliverables.
Requirements
- PhD in physics or a closely related field, or equivalent industry/research experience.
- Expertise in computational physics, scientific computing, or simulation-heavy research is desirable.
- Proficiency with Python, C++, MATLAB, and scientific tools such as ROOT would be ideal.
- Experience working with numerical simulations, Monte Carlo methods, or HPC workflows.
- Strong analytical, quantitative, and mathematical reasoning abilities.
- Demonstrated experience in experimental or scientific data analysis.
- Excellent written and verbal communication skills; ability to convey complex topics to diverse audiences.
Preferred qualifications
- Background in particle physics, astrophysics, plasma/fusion, or quantum systems.
- Experience with AI/ML workflows, scientific benchmarking, or evaluation practices.
- Track record of published research or contributions to scientific literature.