Staff Computational Materials Scientist — Fusion Materials

Cfsenergy; Harvard (MA); On-site; USD 180,000 - 260,000; Full time

Job summary

Commonwealth Fusion Systems seeks a Staff Computational Materials Scientist to advance physics-based models and simulations for materials used in fusion devices. You will work across design and analysis teams to predict properties under extreme environments such as high temperature, radiation, and magnetic fields.

The role involves developing, benchmarking, and applying ICME tools, generating synthetic data, and guiding experiments to reduce uncertainty in materials performance for SPARC and ARC

Qualifications

• PhD or MS in relevant field preferred with strong computational background.
• Experience with ICME tools to answer cross-length-scale questions.
• Proven ability to build physics-based models and validate with data.

Responsibilities

• Use and build computational toolsets to predict material properties in extreme environments.
• Identify microstructural features that affect performance and quantify relationships for optimization.
• Design experiments and simulations to reduce uncertainty in material behavior under extreme conditions.
• Collaborate with engineers to create workflows for data capture, storage, and analysis.

Skills

Computational materials

ICME tools

First-principles modeling

Physics-informed modeling

High-performance computing

Data visualization

Cross-functional collaboration

Education

Materials Science and Engineering

Applied Physics

Applied Mathematics

Physical Chemistry

Computer Science

Tools

Molecular dynamics

Phase-field

FEM

DFT

Parallel computing

Job description

Commonwealth Fusion Systems seeks a Staff Computational Materials Scientist to advance physics-based models and simulations for materials used in fusion devices. You will work across design and analysis teams to predict properties under extreme environments such as high temperature, radiation, and magnetic fields.

The role involves developing, benchmarking, and applying ICME tools, generating synthetic data, and guiding experiments to reduce uncertainty in materials performance for SPARC and ARC