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Faculty Computational Material Science Jobs (NOW HIRING)

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Faculty Computational Material Science information

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$56.5K

$83.1K

$98K

How much do faculty computational material science jobs pay per year?

As of Sep 13, 2026, the average yearly pay for faculty computational material science in the United States is $83,109.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,500.00 and $93,500.00 per year, depending on experience, location, and employer.

What is a faculty in computational material science?

A Faculty in Computational Material Science is an academic professional who teaches, conducts research, and mentors students in the field of computational methods applied to material science. Their work often involves developing and applying computer simulations and theoretical models to understand and predict material properties and behaviors. Faculty members contribute to scientific advancements, publish research findings, and may collaborate with industry and other researchers. They typically hold a PhD in a related field and are employed at universities or research institutions. Their expertise helps train the next generation of scientists and engineers while advancing the field of material science.

What are the key skills and qualifications needed to thrive as a faculty in computational material science?

To thrive as a Faculty in Computational Material Science, you need a strong background in materials science, physics, and computational modeling, typically supported by a Ph.D. in a related field. Expertise in programming languages (such as Python or C++), simulation software (like VASP or LAMMPS), and familiarity with high-performance computing environments is essential. Excellent communication, mentorship, and collaboration skills help in teaching, supervising students, and fostering interdisciplinary research. These skills are crucial for advancing research, securing funding, and effectively educating the next generation of scientists.

What are some common challenges faced by faculty in computational material science, and how can they be addressed?

Faculty in Computational Material Science often encounter challenges such as securing research funding, balancing teaching and research responsibilities, and staying current with rapidly evolving computational tools and methods. Collaborating with interdisciplinary teams and engaging in continuous professional development can help address these challenges. Additionally, building a strong network within the research community and actively seeking partnerships with industry can provide valuable resources and new opportunities for research and career growth.

What is the difference between Faculty Computational Material Science vs Faculty Materials Engineering?

AspectFaculty Computational Material ScienceFaculty Materials Engineering
Required CredentialsPhD in Materials Science, Computational ModelingPhD in Materials Engineering, Applied Physics
Work EnvironmentResearch labs, university classrooms, computational facilitiesResearch labs, manufacturing settings, design studios
Employer & Industry UsageUniversities, research institutes, R&D departmentsManufacturers, aerospace, automotive, industrial firms

Faculty Computational Material Science focuses on theoretical and computational modeling of materials at the atomic and molecular levels, often within academic or research settings. In contrast, Faculty Materials Engineering emphasizes practical application, development, and testing of materials for industrial use. Both roles require advanced degrees but differ in their focus on theory versus application.

What are popular job titles related to Faculty Computational Material Science jobs?

For Faculty Computational Material Science jobs, the most frequently searched job titles are:

Infographic showing various Faculty Computational Material Science job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 75% Physical, 3% Hybrid, and 22% Remote job distribution, with an average salary of $83,109 per year, or $40 per hour.

Computational Materials Scientist - Postdoctoral Researcher

Livermore, CA • On-site

LLNL
Clean Energy Services • 5 - 10K employees

$123K/yr

Full-time

Retirement

Re-posted 28 days ago


Job description

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 to work in the field of computational materials science and actively participate in research dedicated to the discovery of new structural alloys for extreme environments. You will be involved in research to implement methods for materials design and process development. You will be a creative force in the integration and application of thermodynamic and kinetic models into an alloy design framework (including material properties models) that uses numerical optimization methods to rapidly screen for promising compositions over vast multi-component phase spaces. This position is in the Actinide and Lanthanide Science group within the Materials Science Division.

In this role you will

  • Independently develop multicomponent thermodynamic and kinetic databases for inorganic (metal, oxide, carbide, hydride, etc.) systems.
  • Incorporate new Integrated Computational Materials Engineering (ICME) microstructure evolution models and resulting property predictions (ranging from analytical to machine learning surrogate models) in LLNL's Materials Acceleration Platform that is deployed on LLNL's High Performance Computing infrastructure.
  • Develop uncertainty quantification (UQ) and propagation methods into ICME approaches.
  • Interface with experimentalists in design of experiment and material development campaigns.
  • Work both independently and collaborate with others in a multidisciplinary team environment 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.

Qualifications

  • Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. citizenship.
  • PhD in materials science, metallurgy, condensed matter physics, applied math, 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 to perform CALPHAD 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 as reflected in effective presentations at meetings and a demonstrated strong publication record.
  • Initiative and interpersonal skills with desire and ability to work in a collaborative, multidisciplinary team environment.

Qualifications We Desire

  • Experience with artificial intelligence (AI) and/or machine learning (ML) methods.
  • Experience with algorithms relevant for materials design and discovery (Bayesian Optimization, black-box optimization, gradient-based optimization).
  • Experience parameterizing or developing numerical methods for thermodynamic and/or kinetic modeling of phase transformations and microstructure evolution (e.g. CALPHAD, Kampmann-Wagner numerical method, phase field, cellular automata, Monte Carlo method, continuum models).
  • Direct experience with alloy synthesis, processing, and/or characterization; or extensive experience collaborating with experimental colleagues.

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

All your information will be kept confidential according to EEO guidelines.

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire 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.

How to identify fake job advertisements

Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under "Find Your Job" of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.

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.

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