SandboxAQ is seeking a motivated postdoctoral researcher to develop and apply machine learning methods at the intersection of materials science and structural design to support the identification of high-impact design opportunities, the development of lightweighting strategies, and decision-making under complex constraints.
Requirements
- Demonstrated experience applying ML or statistical methods to materials or engineering applications.
- Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit-learn).
- Familiarity with optimization and uncertainty quantification methods such as Bayesian optimization, Gaussian processes, ensemble learning, or related approaches.
- Familiarity with knowledge graphs or graph-based ML for materials/manufacturing data.
- Experience with LLMs for data integration, retrieval-augmented reasoning, or decision support.
- Experience with graph-based, generative, or physics-informed ML for materials or engineering applications.
- Experience working with experimental or simulation-based datasets in materials (e.g., thermomechanical processing data, microstructure characterization, or finite element modeling).
Responsibilities
- Develop and apply ML and optimization techniques to guide lightweighting strategies.
- Use reasoning-based ML approaches to evaluate trade-offs among performance, manufacturability and other criteria.
- Apply Bayesian optimization and related uncertainty-aware methods to balance performance, manufacturability, and other constraints.
- Build reproducible workflows that integrate materials data, manufacturing methods, and simulation outputs.
- Curate and analyze structured datasets on materials, processing routes, and mechanical properties to support ML pipelines.
- Collaborate with engineers and computer scientists to connect ML outputs with structural and materials design tasks.
- Write technical reports and present results to technical and non-technical stakeholders.
Other
- U.S. citizenship is required due to USG contract requirements.
- PhD in Materials Science, Metallurgy, Mechanical Engineering, Computational Materials Science, Applied Physics , or a related field.
- Strong research track record, evidenced by publications in materials science, ML, or computational design.
- Excellent problem-solving and communication skills.
- Ability to work collaboratively in multidisciplinary teams.
