Post-Doctoral Fellow – Materials

GE Vernova; Bengaluru; On-site; INR 900,000 - 1,500,000; Full time

Benefits offered by this job

Relocation assistance

Job summary

GE Vernova Advanced Research seeks a Postdoctoral Research Fellow to advance multiscale modeling and experimental validation for high-temperature materials. This role bridges fundamental materials science with industrial applications, focusing on Ni-based superalloys, Thermal Barrier Coatings, and energy materials, with a 9-month appointment and emphasis on AI/ML in processing-structure relationships. Collaborate across teams to deliver actionable insights.

Qualifications

• Ph.D. or near completion in Materials Science, Metallurgy, or related field (within 6 months of defense).
• Proficiency in continuum modeling and materials property prediction.
• Experience with Python for scientific computing and automation.
• Experience applying AI/ML to materials design and optimization.
• Familiarity with atomistic simulations (DFT/MD) to complement continuum studies.

Responsibilities

• Develop continuum-scale models to simulate material performance and degradation.
• Collaborate with labs to validate inputs and compare simulations with experimental data.
• Apply AI/ML to map processing-structure-property relationships.
• Perform atomistic simulations to inform larger-scale models.
• Contribute to materials design and manufacturing process optimization.

Skills

Python

AI/ML for materials

Continuum modeling

Communication

Multiscale modeling

Data processing

Education

Ph.D. in Materials Science/Metallurgy

Tools

COMSOL Multiphysics

CALPHAD

DFT/MD

Job description

Job Description Summary

GE Vernova Advanced Research is seeking a motivated Postdoctoral Research Fellow to advance our computational and experimental capabilities. This role focuses on bridging the gap between fundamental materials modeling and industrial application. The successful candidate will apply multiscale physics to predict the performance and degradation of high-temperature structural materials, including Ni-based superalloys and Thermal Barrier Coatings (TBCs), as well as various energy-related materials. The role involves integrating simulation insights with experimental validation to drive the development of next-generation materials, including the optimization of existing materials for critical supply chains and the design of novel manufacturing pathways.

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.
• This role is for a fixed duration of 9 months.

Required Qualifications

• Education: Recently completed Ph.D/Submitted Ph.D Thesis, in Materials Science, Metallurgy, or a related field (< 6 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.

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