Expert Work
Computational Materials Science Expert
Status: Open
- Pay
- $175/hr
- Location
- Remote
- Employment type
- Contract
- Posted
- 2026-04-16
About this opportunity
This is a remote, project-based role for machine learning professionals with deep expertise in computational materials science. You will complete tasks at the intersection of ML and materials research — including model development, simulation data analysis, and research tasks applied to real atomistic, electronic, or structural materials datasets. Work is over the next 2–3 weeks, asynchronous, and assigned on a project-by-project basis, with an expected commitment of 10–20 hours per week for the projects you accept. This position offers exceptional pay, exposure to cutting-edge materials research problems, and a strong addition to your research portfolio.
What you'll do
- Apply machine learning techniques to materials science problems, including property prediction, materials discovery, and structure-property relationship modeling
- Build, train, and evaluate ML models trained on simulation data such as DFT, molecular dynamics, or Monte Carlo outputs
- Develop predictive models using supervised and unsupervised learning approaches relevant to materials systems
- Contribute to materials informatics workflows integrating ML with high-throughput computational pipelines
- Document methodologies, model assumptions, and technical approaches clearly and reproducibly
Requirements
- Published researcher with at least one first-author publication in a peer-reviewed journal
- Demonstrated expertise in both machine learning and computational materials science (e.g., DFT, force field development, atomistic simulation, or materials informatics)
- Strong problem-solving skills and ability to work independently on technical tasks
- Master's or PhD in Materials Science, Computational Chemistry, Physics, Computer Science, or a related quantitative field
Preferred qualifications
- Background in TA'ing or teaching computational physics, chemistry, or materials science courses
- Familiarity with materials-focused ML frameworks (e.g., CGCNN, MatGL, M3GNet, ALIGNN, or similar graph neural network approaches)
- Experience with computational materials tools and frameworks (e.g., VASP, Quantum ESPRESSO, LAMMPS, ASE, or similar)