材料科学专家

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.

Solve and validate computational materials-science and materials-engineering problems.

Create material structures, atomic configurations, compositions, and solver-ready inputs.

Model relationships between composition, structure, processing, properties, and performance.

Run atomistic, electronic-structure, molecular-dynamics, continuum, electrochemical, or related simulations.

Analyze mechanical, thermal, electrical, chemical, structural, or electrochemical properties.

Diagnose failed calculations, invalid structures, convergence problems, numerical instability, and incorrect physical assumptions.

Compare computational results with experimental data, literature values, known properties, or expected physical trends.

Review AI-generated solutions for scientific correctness and identify invalid assumptions, configurations, or conclusions.

Develop reproducible reference solutions and objective verification methods.

An MS or PhD in Materials Science and Engineering, Metallurgy, or a closely related discipline; or

An MS or PhD in Mechanical Engineering or Chemical Engineering with a substantial materials specialization.

Strong understanding of materials behavior and relevant structure-property relationships.

Experience with computational materials modeling, simulation, characterization, or materials-focused engineering analysis.

Practical proficiency with Python.

Experience with at least one engineering or scientific tool that can be operated through a CLI, scripting interface, configuration files, or programmatic API.

Ability to understand and justify modeling assumptions, parameters, approximations, and convergence criteria.

Ability to distinguish computational failures from genuine physical behavior.

Ability to explain complex scientific reasoning and technical limitations clearly.

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.

Experience may come from academic research, national laboratories, industry R&D, computational engineering, or other demonstrated materials work.

Apply to the role and complete the screening questions.

Complete an AI interview of approximately 30 minutes.

Complete a technical assessment, if required.

Complete the hiring manager review.

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.

We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.

- Advanced degree (PhD preferred) in materials science, materials engineering, or a closely related field (e.g., physics, chemistry, chemical engineering)

- Deep expertise in computational materials science, including atomistic, electronic-structure, molecular-dynamics, continuum, or electrochemical simulation methods

- Strong understanding of material structures, properties, processing, performance, and failure mechanisms

- Proficiency in Python for scripting, automating simulations, data processing, and validation

- Experience using scientific/engineering tools programmatically (e.g., via APIs, command-line interfaces, or configuration files); GUI-only experience is insufficient

- Ability to create, solve, and validate engineering tasks involving material models and simulations

- Skill in diagnosing simulation failures, numerical instability, convergence issues, and invalid physical assumptions