What This Opportunity InvolvesYou design computational physics problems to challenge a frontier AI model. The problem must have an answer that is verifiable by code, and it has to require a specialized tool like FEniCS, OpenFOAM, Meep, REBOUND, CAMB, or others. Generic numerical libraries on their own won’t cut it. Each problem runs inside a sealed Linux container with the tool pre-installed and a programmatic judge that grades the model’s answer.As an expert author, you:Pick an anchor tool and design a problem that hinges on its physics models, integrators, Monte Carlo kernels, or PDE discretisations.Write a Python reference solution, supply input files and domain or initial condition definitions where needed.Decide the numerical answer and how close the model needs to get — with a domain‑appropriate tolerance — to count as right.Test the problem against the model in batches of parallel attempts, tuning the problem difficulty until the agent only succeeds in a small number of attempts.Once you’re happy with the task, and it scores within range, the task goes to a senior reviewer in your subfield to provide feedback to ensure task quality is high.Calibration requires patience. You’re tuning the problem against batches of parallel runs of the agent, aiming for a pass rate in the 10‑30% band. Reaching that means rewriting field configurations, tightening initial conditions and solver parameters, and watching how the agents act. You’ll learn how these agents cut corners, where a simulation stalls, where an integrator converges. This time compounds in two directions:



you come out of each task with deeper command of the anchor tool itself, and you also gain a hands‑on working intuition for how a frontier model navigates complex electromagnetic, fluid, gravitational, and cosmological problems.What We Look ForThis opportunity is a good fit for physicians with experience in Python and open to part‑time, non‑permanent projects.Degree in Physics (Theoretical, Experimental, or Computational) or related field.2+ years of research, applied, or teaching experience.Python proficiency for writing reference solutions.Fluency with — or solid willingness to independently learn — at least one scriptable physics package: FEniCS / DOLFINx, OpenFOAM, Meep, MPB, openEMS, Geant4, PYTHIA8, ROOT / PyROOT, WarpX, REBOUND, MESA, CAMB, CLASS, or bilby.Ability to design problems that genuinely require a specialized simulation tool.Strong written English (C1+).No prior experience with the listed tools? You’re still welcome to apply — as long as you’re ready to get up to speed on your own and hit the ground running.Project Time ExpectationsThe tasks are estimated to require around 10‑20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.CompensationContributors can earn up to $35 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.#J-18808-Ljbffr

📌 Physics & Python Expert - Freelance Ai Trainer - Up To $35 An Hour - Part Time - Remote (Winnipeg)
🏢 Mindrift
📍 Winnipeg