Company Description

Join us and make YOUR mark on the World!

Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability. 

Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact.

Job Description

We have an opening for a Postdoctoral Researcher in the areas of atomistic and multiscale modeling to optimize metal joining in alloyed systems. The overarching goal of this project is to develop an integrated simulation-experiment workflow to maximize the utility of lab-scale tests by elucidating the fundamental scientific principles governing metal part joining. You will actively participate in the development of Machine-learning interatomic potentials (MLIP) for large-scale atomistic modeling with quantum-mechanical accuracy for use in Grand Canonical grain boundary interface searches and the determination of subsequent interfacial properties. This work will occur in conjunction with mesoscale modeling and experimental teams to determine the effects of interfacial defects, impurities, phase transformations, and transient liquid phases on joining strength. The position is in the Materials Dynamics and Kinetics group of the Materials Science Division in the Physical & Life Sciences Directorate, supporting the Energetic Materials Center (EMC).

Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week. 

You will

• Perform and analyze first principles-based simulations (e.g., DFT, DFTB, etc.) to generate reference and validation data.
• Develop GPU-enabled MLIP models for complex intermetallic and alloyed systems for use in grain boundary and interface modeling.
• Provide inputs for mesoscale and thermodynamic modeling approaches, including mass transport properties and interfacial energies.
• Development and maintenance of our advanced atomistic simulation codes for interatomic model optimization and simulation.
• Aid in maintaining an organized and accessible materials data base for ML-driven experimental predictions.
• Document research; write and publish papers in peer-reviewed journals, and present results within the DOE community, at working group meetings, and at conferences.
• Pursue independent but complementary research interests and interact with a broad spectrum of scientists internally and externally to the Laboratory.
• Perform other duties as assigned.

Qualifications

• Ability to secure and maintain a U.S. DOE Q-level security clearance, which requires U.S. Citizenship.
• PhD in Materials Science, Condensed Matter Physics, Chemistry, Chemical Engineering, or a related field.
• Experience with scientific code development, including programming in python, C++, C, Fortran, and/or an equivalent level compiled language.
• Experience with electronic structure calculations using packages such as VASP, Quantum Espresso, or similar codes.
• Experience with molecular dynamics model development and applying atomistic machine learning methods for problems in chemistry or materials science.
• Experience with large-scale atomistic simulations in high-performance computing environments.
• Ability to develop independent research projects through publication of peer-reviewed literature.
• Proficient verbal and written communication skills as reflected in effective presentations at seminars, meetings and/or teaching lectures.
• Self-motivated and excellent interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment.

Qualifications We Desire

• Experience with statistics-based simulations including (kinetic) Monte Carlo and enhanced sampling methods.
• Experience with computational workflow development and multiscale modeling integration.
• Experience with simulations of structurally/chemically disordered grain boundaries and heterogeneous interfaces.
• Experience with SI (Super Intelligence, formerly referred to as Artificial Intelligence, or AI) and/or ML methods including surrogate modeling, feature engineering, generative models, Bayesian optimization, uncertainty quantification, sensitivity analysis, and Pareto front analysis.

Pay Range

$123,048 Annually 

This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting.  An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs.

Additional Information

#LI-Hybrid

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years. The postdoctoral appointment is a temporary, mentored training position intended for individuals in the early stages of their postdoctoral research careers. Consistent with the structure of the program, applicants must have received their Ph.D. (or equivalent doctorate) within five years of the anticipated start date.

Why Lawrence Livermore National Laboratory?

• Included in 2026 Best Places to Work by Glassdoor!
• Flexible Benefits Package
• 401(k)
• Relocation Assistance
• Education Reimbursement Program
• Flexible schedules (*depending on project needs)
• Our values - visit https://www.llnl.gov/inclusion/our-values

Security Clearance

This position requires a Department of Energy (DOE) Q-level clearance.  If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing.  Q-level clearance requires U.S. citizenship. 

Pre-Employment Drug Test

External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.

Wireless and Medical Devices

Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession.  This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.  

If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas.  Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.

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To learn more about recruitment scams: https://www.llnl.gov/sites/www/files/2023-05/LLNL-Job-Fraud-Statement-Updated-4.26.23.pdf

Equal Employment Opportunity

We are an equal opportunity employer that is committed to providing all with 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 create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory.  If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request. 

California Privacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.