PhD Physical Sciences Expert - Remote Scientific Tasks
Listing checked October 1, 2026 · pay as published by Turing
Overview
Turing invites physical sciences experts to join Terminal Bench Science, where you convert real physics, chemistry, and materials science workflows into terminal-based benchmark tasks. You will build self-contained computational environments with pinned dependencies, write executable solutions in Python, Bash, C/C++, or Julia, and design automated tests that check numerical accuracy and physical consistency. The work covers domains such as thermodynamics, quantum mechanics, statistical mechanics, molecular dynamics, and computational chemistry. Your tasks will measure whether AI agents can reason through scientific problems, operate command-line tools, and produce trustworthy scientific artifacts. Reviewers depend on your clear notes about scientific assumptions and computational limits.
What You'll Do10
- 1Design multi-step terminal tasks that mirror genuine physical-science workflows.
- 2Build self-contained computational environments with pinned dependencies and scientific software.
- 3Produce input datasets, molecular structures, simulation parameters, experimental data, or model configurations.
- 4Implement expert solutions using Python, Bash, C/C++, Julia, or domain-specific tools.
- 5Write automated tests for numerical accuracy, physical consistency, convergence, and output structure.
- 6Create tasks involving simulations, numerical modeling, data fitting, optimization, spectroscopy, molecular analysis, or scientific visualization.
- 7Define numerical tolerances, units, boundary conditions, and expected scientific behavior.
- 8Verify that tasks run successfully without runtime downloads and stay reproducible.
- 9Debug dependency issues, precision problems, solver stability, performance bottlenecks, and file format errors.
- 10Document scientific assumptions and computational limitations for reviewers.
Requirements6
- 1Ph.D., postdoctoral experience, or equivalent advanced technical background in a relevant physical-science discipline.
- 2Strong programming ability in Python, C/C++, Julia, Bash, or another scientific language.
- 3Hands-on work in Linux or terminal-based environments.
- 4Experience with numerical methods, scientific modeling, simulations, or quantitative data analysis.
- 5Proven ability to create and validate computational scientific workflows on your own.
- 6Solid grasp of units, numerical precision, physical constraints, and scientific reproducibility.
Who Should Apply
The ideal candidate holds a Ph.D. or postdoctoral background in physics, chemistry, materials science, or a related field, and writes code in Python, C/C++, Julia, or Bash as comfortably as they discuss numerical methods. You have built simulations, run data fitting, or analyzed quantitative results in a Linux terminal, and you can package that work into reproducible tasks with clear assumptions. Familiarity with NumPy, SciPy, OpenMM, ASE, RDKit, Psi4, LAMMPS, or GROMACS will help you move faster. This role is less suitable if you prefer wet-lab experiments, avoid command-line workflows, or have never written automated tests for scientific code. Candidates often score low when they describe research in broad terms without showing code, or when they cannot explain how units and tolerances affect task validity.
Pay and demand for Quality Assurance & Testing roles
AggregatedTypical pay
$75/hour
This role
Pay not disclosed
Most Quality Assurance & Testing roles pay $55–$95 per hour.
Based on 387 similar roles that publish pay · 123 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
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Compensation
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Required Skills
Application Tip
Highlight one concrete scientific workflow you have turned into a reproducible script or package. Name the exact libraries and tools you used, for example Python with NumPy and SciPy, or GROMACS with Bash, and mention how you tested numerical accuracy or convergence. Quantify the result, such as runtime, precision, or dataset size, to show that your work is validated and portable.
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