SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Commonwealth Fusion Systems is seeking a Senior Computational Materials Scientist to join the Materials and Processing Department in support of SPARC, a net-energy tokamak under construction in Devens, MA, and the future ARC fusion power plant. The role focuses on developing physics-informed computational models and simulations to predict material behavior in extreme fusion environments—including high neutron fluxes, extreme temperatures, high magnetic fields, and cryogenic conditions.
Key Responsibilities:
- Develop and implement computational toolsets and workflows to predict material properties in extreme environments with uncertainty quantification informed by physical mechanisms
- Identify and quantify chemical and microstructural features that impact material performance under simultaneous corrosion, stress, irradiation, magnetic field, and high-temperature exposure
- Design experiments for materials testing in extreme conditions, collaborating with design and analysis engineers
- Develop material design curves and statistical methodologies for tokamak applications (fatigue, dielectric breakdown, radiation effects)
- Build predictive models for material property and microstructure evolution
- Identify knowledge and experimental capability gaps addressable through simulation
- Benchmark material property variability across manufacturing and fabrication processes
- Collaborate on data capture, storage, visualization, and analysis workflows for materials research
- Support materials standard specifications and stay current with evolving modeling techniques
The Materials and Processing Department supports design engineers, supply chain, and manufacturing by selecting materials, defining processing routes, validating properties, and conducting R&D to enable fusion power deployment. The work addresses unique challenges posed by deuterium-tritium fusion reactors, molten salt coolant systems, complex component geometries, and extreme mechanical loading in cryogenic conditions.
Requirements:
- Bachelor's degree in Materials Science and Engineering, Applied Physics, Applied Mathematics, Physical Chemistry, Computer Science, or related field
- Minimum 7+ years of experience in experimental or computational materials science
- Expertise in computational materials science tools and integrated computational materials engineering (ICME) methodologies
- Implementation and/or authorship of codes for material property and microstructure calculations and analyses
- Demonstrated experience building predictive models for material properties and microstructure evolution in extreme environments
- Model validation using experimental data or physical mechanism bounds
- Deep understanding of process-structure-properties-performance principles and fundamental mechanisms driving property changes in extreme environments
- Knowledge of materials simulation methods across length scales: atomistic, molecular dynamics, density functional theory, Monte Carlo, phase field, crystal plasticity, dislocation dynamics, computational thermodynamics, and finite element methods
- Strong scientific computing foundation: numerical algorithms, FEM, FD, FVM, BEM, eigen/linear/PDE solvers, convex optimization, computational geometry, advanced statistical methods
- High-performance computing experience: distributed processing, message passing interface, GPU acceleration
- Understanding of uncertainty sensitivity in model inputs and outputs
- Demonstrated ability to work effectively cross-functionally with excellent organizational and prioritization skills
- Ability to decompose complex problems into smaller, value-adding deliverables
- Physical capability to perform typing and sitting for extended periods
- Dedication to safety in industrial environments with potential hazards (heat, cold, noise, fumes, strong magnets, lead, high voltage, cryogenics)
- Willingness to travel occasionally (less than 10%) and work required nights/weekends/on-call as needed
Bonus qualifications: Experience presenting complex quantitative data clearly and intuitively; decision-making in fast-paced engineering settings; technical communication with engineering teams; driving under-defined problems to completion; operating in flexible, service-oriented team environments.
