C
Crossing Hurdles
Physical Sciences Expert Terminal Bench
Skills
PythonNumPypandasData AnalysisLinux
18 days ago
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Important information
Contract type:
Permanent
Salary:
₹6,000,000 – ₹7,500,000 / year
Location:
Gurugram, Haryana, India
Work mode:
Remote
Published on:
7 October 2026
What they need
Role & responsibilities
- Design multi-step terminal-based tasks based on realistic physical-science workflows.
- Develop self-contained computational environments with pinned dependencies and scientific software.
- Create input datasets, molecular structures, simulation parameters, experimental data, and model configurations.
- Implement expert solutions using Python, Bash, C/C++, Julia, or domain-specific scientific tools.
- Develop automated tests for numerical accuracy, physical consistency, convergence, and output structure.
- Create tasks involving simulations, numerical modeling, data fitting, optimization, spectroscopy, molecular analysis, and scientific visualization.
- Define numerical tolerances, units, boundary conditions, and expected scientific behavior.
- Validate that tasks are reproducible and execute successfully without runtime downloads.
- Debug dependencies, precision, solver stability, performance, and file-format issues.
Preferred candidate profile
- Ph.D., postdoctoral experience, or equivalent advanced technical experience in a relevant physical-science discipline.
- Strong expertise in Physics, Chemistry, Materials Science, Computational Physics, Computational Chemistry, Astronomy and Cosmology, Thermodynamics, Quantum Mechanics, Statistical Mechanics, or Materials Modeling.
- Strong programming skills in Python, C/C++, Julia, Bash, or another scientific programming language.
- Experience working in Linux or terminal-based environments.
- Experience with numerical methods, scientific modeling, simulations, or quantitative scientific data analysis.
- Ability to independently create and validate computational scientific workflows.
- Strong understanding of units, numerical precision, physical constraints, and scientific reproducibility.
- Familiarity with scientific libraries such as NumPy, SciPy, pandas, matplotlib, SymPy, or JAX is preferred.
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