Post Doctoral Fellow - Materials

Job Description Summary

Job Description

Roles and Responsibilities

• Develop and execute continuum-scale models to simulate material performance, transport phenomena, and degradation kinetics.
• Partner with laboratory teams to characterize material properties, validate model inputs, and correlate simulation predictions with empirical data from in-house tests.
• Apply your knowledge in Mechanical & Materials engineering to work on innovative solutions for the GE Vernova product portfolio.
• Learn required tools/processes and perform advanced simulations, validating models against published external results and in-house experimental data.
• Deliver on assigned project tasks with minimal guidance, document results, and provide actionable insights for material optimization.
• Collaborate with the team to improve materials management, evaluate existing materials for critical supply chains, and enhance sustainability for GE Vernova products.
• Perform literature surveys and assess the state-of-the-art landscape in material technologies, design, and manufacturing to guide R&D project direction.
• Apply AI/ML techniques to map processing-structure-property relationships to accelerate the discovery and improvement of advanced materials.
• Utilize atomistic simulations as a complementary tool to resolve fine-scale mechanisms that inform and refine larger-scale models.
• Apply systems thinking to understand the impact of material behavior at the component and system levels and actively participate in team efforts to achieve collective project goals.

Qualifications / Requirements

• Education: Recently completed Ph.D/Submitted Ph.D Thesis, in Materials Science, Metallurgy, Mechanical Engineering or a related field ing (< 11 months post completion of Ph.D Defense)
• Modeling Expertise:
  • Advanced proficiency in continuum modeling software, specifically COMSOL Multiphysics, and experience with other relevant commercial or open-source simulation packages.
  • Demonstrated experience in property prediction models and thermodynamic/kinetic modeling (e.g., CALPHAD).
  • Proficiency in Python for scientific programming, data processing, and model automation.
  • Experience applying AI/ML techniques for material property prediction and design optimization.
  • Familiarity with atomistic simulation methods (e.g., DFT, MD) to supplement continuum studies.
• Experimental Background: Proven experience in a laboratory setting with the ability to interpret characterization data to inform modeling parameters.
• Domain Knowledge: Solid foundation in metallurgy and high-temperature materials. Familiarity with energy materials, fuel cells, or energy storage technologies is an asset. Expertise in material design and manufacturing processes.
• Communication: Strong ability to collaborate across multidisciplinary teams and articulate complex technical findings to diverse stakeholders.
• Excellent oral and written communication skills
• Demonstrated ability to work independently and in a team environment and accountability/ownership of assigned tasks
• Demonstrated ability to take up challenging assignments and deliver.
• Passion to innovate and creative problem solving
• Ability to do a State-of-the-Art Survey on and awareness/engagement with leading research groups globally in the assigned areas and synthesis the findings.
• This position is for a fixed term of 9 months

Desired Characteristics

• Experience with advanced modeling techniques such as phase-field or crystal plasticity.
• Understanding of challenges related to material availability and design strategies for critical supply chains.
• Self-motivated with a strong track record of peer-reviewed publications and conference presentations.
• Demonstrated ability to work effectively in a team-based environment, with a commitment to cross-functional collaboration and systems-level problem solving.

Additional Information

Relocation Assistance Provided: Yes