Computational Physics Simulation Template Specialist

About OpenTrain

OpenTrain AI is the hiring and contracting organization for this role. OpenTrain is the #1 platform for finding and building careers in AI training and data labeling, helping specialists discover projects, build a credible profile, and apply in minutes.

This is a worldwide, remote contractor opportunity with part-time scheduling and a commitment of 20+ hours per week. Creating an OpenTrain account is free.

• Work remotely from anywhere worldwide
• Join a fast-growing field connecting expert knowledge with AI development
• Build a lasting portfolio of specialized AI training work

About AI Training and Simulation Benchmarking

AI training is the human side of building artificial intelligence. People create examples, evaluate outputs, and prepare reference materials that help AI systems become more capable and reliable.

In this role, your computational physics expertise will support benchmarking for scientific and engineering software. Your simulations, reference solutions, and physical reasoning will help evaluate whether AI systems can produce accurate results in demanding technical domains.

• Apply real-world scientific expertise to cutting-edge AI training
• Create examples and evaluations that reflect authentic computational workflows
• Help define what technically correct simulation results look like

The Role

OpenTrain is recruiting a Computational Physics Simulation Template Specialist to support AI training work focused on computational physics simulation benchmarking. You will create realistic simulation materials and reference solutions that evaluate AI systems on scientific and engineering applications.

Prior AI training experience is not required. The most important qualification is strong real-world knowledge of computational physics and physical modeling. The role is classified as entry level in the structured listing, while the requirements call for at least five years of professional modeling and simulation experience.

• Employment type: Contractor and part time
• Time requirement: 20+ hours per week
• Language: Clear written English
• Work location: Worldwide and remote
• Subject matter: Computational physics simulation benchmarking

What You'll Do

You will map the real capabilities of scientific applications and create representative materials that can be used to assess AI-generated work. The role combines simulation execution, technical documentation, reference-solution development, and careful evaluation of physical correctness.

You will also document the physical reasoning behind boundary conditions, simulation fields, and other results so engineering teams can implement reliable checks.

• Map supported input types, important operations, and available outputs in scientific applications
• Create Gaussian input and log files
• Build OpenFOAM case dictionaries and WRF namelist decks
• Create FITS astronomical image files and SAC seismic trace files
• Prepare NetCDF climate datasets
• Complete representative tasks and reference solutions
• Define correctness criteria for simulation outputs
• Explain the physical reasoning behind boundary conditions and simulation fields

Required Qualifications

You should have professional expertise in computational physics, thermofluids, seismology, or atmospheric modeling, along with demonstrated experience running real physics simulations. Strong written English and careful attention to detail are required for documenting application capabilities and reference solutions.

• At least five years of professional modeling and simulation experience
• Hands-on experience with one or more of Gaussian, OpenFOAM, SU2, Elmer FEM, or WRF
• Strong understanding of differential equations and boundary conditions
• Strong understanding of wave propagation and solver convergence tolerances
• Ability to explain why a physical boundary condition or simulation field result is correct
• Clear written English for technical documentation
• Careful attention to detail

Preferred Experience

Experience with tools for seismic analysis, astronomical image inspection, climate data processing, or Python-based automation will strengthen your ability to create and review simulation templates.

• ObsPy
• SeisComP
• SAOImage DS9
• NetCDF tools
• Python automation for simulation input generation
• Python automation for simulation post-processing

Why This Work Matters

Every major AI system depends on human-prepared examples, evaluations, and feedback. Specialists who understand scientific software and physical modeling can help shape how AI systems reason about technical problems.

OpenTrain gives you a place to build a profile around this expertise and turn specialized AI training projects into a longer-term professional portfolio.

• Work at the intersection of computational physics and AI development
• Use expertise from real simulation practice rather than requiring prior AI training experience
• Contribute to reliable evaluation of scientific and engineering software

How to Apply

Create a free OpenTrain account, build your profile around your computational physics and simulation experience, and apply through OpenTrain. Highlight the simulation tools you have used, the physical systems you have modeled, and your experience documenting correct results.

• Create or update your OpenTrain profile
• Show your experience running real computational physics simulations
• List relevant tools such as Gaussian, OpenFOAM, SU2, Elmer FEM, or WRF
• Apply in minutes through OpenTrain