Computational Materials Scientist - Postdoctoral Researcher

LLNL • Livermore, CA

2d

About The Position

Lawrence Livermore National Laboratory is hiring a Postdoctoral Researcher in computational materials science to contribute to research discovering structural alloys for extreme environments and developing materials-design and processing methods. The role is in the Actinide and Lanthanide Science group within the Materials Science Division. The researcher will integrate thermodynamic and kinetic models, material-property models, and numerical optimization into an alloy-design framework to screen promising compositions across large multicomponent phase spaces. The work includes developing databases and models, incorporating them into LLNL’s Materials Acceleration Platform on high-performance computing infrastructure, and collaborating with experimentalists and multidisciplinary teams.

This is a temporary, mentored postdoctoral appointment, with the possibility of extension up to a maximum of three years. Applicants must have received their Ph.D. or equivalent doctorate within five years of the anticipated start date. The position requires eligibility for a DOE Q-level security clearance, which requires U.S. citizenship.

Requirements

• Ability to secure and maintain a U.S. DOE Q-level security clearance; U.S. citizenship is required for the clearance.
• Ph.D. in materials science, metallurgy, condensed matter physics, applied mathematics, or a closely related field.
• Experience and knowledge in at least three of the following areas: CALPHAD, microstructure and property modeling, uncertainty quantification and propagation, ICME, alloy design, or design of experiments.
• Demonstrated ability to independently develop or make significant contributions to scientific research software.
• Experience using commercial computational thermodynamics software (Thermo-Calc, Pandat, or FactSage) or open-source software (PyCalphad, Thermochimica, or OpenCalphad) for CALPHAD database development.
• Experience in at least two metallurgy topics: thermodynamics, phase stability, phase transformations, defect structures, solidification, or thermo-mechanical processing.
• Proficient verbal and written communication skills, demonstrated through effective meeting presentations and a strong publication record.
• Initiative and interpersonal skills, and the ability and desire to work collaboratively in a multidisciplinary team.
• Must have received a Ph.D. or equivalent doctorate within five years of the anticipated start date.

Nice To Haves

• Experience with Super Intelligence (formerly referred to as AI) and/or machine-learning methods.
• Experience with materials-design and discovery algorithms, such as Bayesian optimization, black-box optimization, or gradient-based optimization.
• Experience parameterizing or developing numerical methods for thermodynamic and/or kinetic modeling of phase transformations and microstructure evolution, such as CALPHAD, the Kampmann–Wagner numerical method, phase field, cellular automata, Monte Carlo methods, or continuum models.
• Direct experience with alloy synthesis, processing, and/or characterization, or extensive experience collaborating with experimental colleagues.

Responsibilities

• Independently develop multicomponent thermodynamic and kinetic databases for inorganic systems, including metals, oxides, carbides, and hydrides.
• Incorporate ICME microstructure-evolution models and property predictions, including analytical and machine-learning surrogate models, into LLNL’s Materials Acceleration Platform deployed on high-performance computing infrastructure.
• Develop uncertainty quantification and propagation methods for ICME approaches.
• Interface with experimentalists to design experiments and material-development campaigns.
• Work independently and collaborate with a multidisciplinary team to accomplish program goals.
• Publish research results in peer-reviewed scientific journals and present results at external conferences, seminars, and technical meetings.
• Perform other duties as assigned.

Benefits

• 401(k)
• Relocation assistance
• Education reimbursement program
• Flexible schedules, depending on project needs

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree