Computational Materials Scientist - Postdoctoral Researcher

LLNL • Livermore, CA

3d

About The Position

Lawrence Livermore National Laboratory is seeking a Postdoctoral Researcher in computational materials science to conduct research focused on discovering structural alloys for extreme environments and implementing methods for materials design and process development. The researcher will integrate and apply thermodynamic and kinetic models, including material property models, within an alloy design framework that uses numerical optimization to screen promising compositions across large multicomponent phase spaces. The position is in the Actinide and Lanthanide Science group within the Materials Science Division.

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

Requirements

• Ph.D. in materials science, metallurgy, condensed matter physics, applied mathematics, or a closely related field.
• Ability to secure and maintain a U.S. DOE Q-level security clearance; U.S. citizenship is required.
• Experience and knowledge in at least three of the following: CALPHAD, microstructure and property modeling, uncertainty quantification and propagation, ICME, alloy design, and 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, including effective meeting presentations and a strong publication record.
• Initiative and interpersonal skills, with the desire and ability to work collaboratively in a multidisciplinary team.
• Ph.D. or equivalent doctorate received within five years of the anticipated start date.

Nice To Haves

• Experience with Super Intelligence (SI), formerly referred to as artificial intelligence (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, from analytical models to 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.
• Collaborate with experimentalists on design of experiments and material-development campaigns.
• Work independently and collaborate with a multidisciplinary team to accomplish program goals.
• Publish research in peer-reviewed scientific journals and present results at external conferences, seminars, and technical meetings.
• Perform other duties as assigned.

Benefits

• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (depending on project needs)

What This Job Offers

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