Computational Materials Scientist - Postdoctoral Researcher

Lawrence Livermore National Laboratory; Livermore (CA); On-site; USD 110,743 - 135,352; Full time

Benefits offered by this job

Flexible schedules

Included in 2026 Best Places to Work by Glassdoor

Job summary

Lawrence Livermore National Laboratory seeks a Postdoctoral Researcher in computational materials science, focusing on discovering new structural alloys for extreme environments. The role involves developing thermodynamic databases, integrating ICME models, and applying numerical optimization methods to screen compositions.

Applicants must have a PhD in relevant fields and experience in thermodynamics and modeling techniques. The position offers a competitive salary and the opportunity for a collaborative work environment.

Qualifications

• Ability to secure and maintain a US DOE Q-level security clearance, requiring US citizenship.
• Demonstrated ability to independently develop or make significant contributions to scientific research software.
• Proficient verbal and written communication skills with a strong publication record.

Responsibilities

• Independently develop multicomponent thermodynamic and kinetic databases for inorganic systems.
• Incorporate new ICME microstructure evolution models into LLNL’s Materials Acceleration Platform.
• Develop uncertainty quantification and propagation methods for ICME approaches.

Skills

CALPHAD

ICME

Microstructure and property modeling

Uncertainty quantification and propagation

Alloy design

Design of experiments

Education

PhD in materials science, metallurgy, condensed matter physics, or closely related field

Tools

Thermo‑Calc

Pandat

FactSage

PyCalphad

Thermochimica

OpenCalphad

Job description

Computational Materials Scientist - Postdoctoral Researcher

Postdoctoral Researcher position – Livermore, CA (Full‑time) – Posting date: 06/16/2026

Job Description

We are seeking a Postdoctoral Researcher to work in computational materials science focused on discovering new structural alloys for extreme environments. The role involves developing multicomponent thermodynamic and kinetic databases, integrating ICME models, and applying numerical optimization methods to rapidly screen promising compositions over vast multi‑component phase spaces. The position is within the Actinide and Lanthanide Science group of the Materials Science Division.

Responsibilities

• Independently develop multicomponent thermodynamic and kinetic databases for inorganic systems (metal, oxide, carbide, hydride, etc.).
• Incorporate new ICME microstructure evolution models and resulting property predictions into LLNL’s Materials Acceleration Platform on the High Performance Computing infrastructure.
• Develop uncertainty quantification and propagation methods for ICME approaches.
• Interface with experimentalists in design of experiments and material development campaigns.
• Work independently and collaborate with a multidisciplinary team to accomplish program goals.
• Publish research results in peer‑reviewed journals and present at conferences, seminars, and technical meetings.
• Perform other duties as assigned.

Qualifications

• Ability to secure and maintain a US DOE Q‑level security clearance, requiring US citizenship.
• PhD 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, design of experiments.
• Demonstrated ability to independently develop or make significant contributions to scientific research software.
• Experience with commercial (Thermo‑Calc, Pandat, or FactSage) or open‑source (PyCalphad, Thermochimica, or OpenCalphad) computational thermodynamics software for database development.
• Experience in at least two of the following metallurgy topics: thermodynamics, phase stability, phase transformations, defect structures, solidification, or thermo‑mechanical processing.
• Proficient verbal and written communication skills with a strong publication record.
• Initiative and interpersonal skills with the desire to work in a collaborative, multidisciplinary environment.

Desired Qualifications

• Experience with artificial intelligence or machine learning methods.
• Experience with Bayesian, black‑box, or gradient‑based optimization algorithms for materials design.
• Experience parameterizing or developing numerical methods for thermodynamic and kinetic modeling of phase transformations and microstructure evolution.
• Direct experience with alloy synthesis, processing, and/or characterization, or extensive collaboration with experimental colleagues.

Security & Background

DOE Q‑level clearance is required. Applicants must be US citizens and will undergo a federal background investigation.

Pre‑employment Drug Test

External applicants selected for this position are required to pass a pre‑employment drug test, which includes testing for marijuana in compliance with federal law.

Pay Range

$123,048 annually

Position Information

Postdoctoral appointment with a possibility of extension up to a maximum of three years, open to candidates who have earned a PhD at the time of hire.

Benefits

• Included in 2026 Best Places to Work by Glassdoor.
• Flexible schedules, subject to project needs.

Equal Opportunity Employer

We are an equal‑opportunity employer committed to providing a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

Reasonable Accommodation

Our goal is to provide an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory. If you need a reasonable accommodation during the application or recruiting process, please submit a request through our online form.

Privacy

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