We are looking for a highly skilled Materials Science Expert to contribute to an AI training project involving computational materials science, materials modeling, scientific simulation, and Python.
The work involves creating, solving, reviewing, and validating engineering tasks related to material structures, properties, processing, performance, and failure. A representative task may require constructing a material or atomic model, configuring and running a simulation, calculating relevant properties, analyzing the resulting outputs, and determining whether the solution is computationally valid and physically meaningful.
This role requires both strong materials expertise and experience using engineering or scientific tools programmatically. Experience limited exclusively to graphical user interfaces will not be sufficient, as task solutions must be reproducible through code, scripts, configuration files, or command-line tools.
Relevant tools may include LAMMPS, ASE, pymatgen, Quantum ESPRESSO, FEniCSx, CalculiX, Elmer, PyBaMM, or similar programmatic materials and simulation software. Experience with an equivalent CLI-accessible tool is acceptable.
Relevant Python tools may include NumPy, SciPy, pandas, Matplotlib, Jupyter, atomistic modeling packages, materials informatics libraries, or domain-specific scientific tools. No single library is mandatory.
Experience may come from academic research, national laboratories, industry R&D, computational engineering, or other demonstrated materials work.
Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert's experience and workflow.
Minimum submission requirements apply.
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