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Intern Computational Materials Science Jobs (NOW HIRING)

AI Materials Research Engineer

Santa Clara, CA ยท On-site

$170K - $234K/yr

MS/PhD in Materials Science, Computational Materials Science, Physics, Chemical Engineering, or related field. * 2-5 years of experience in Computational Materials Science, Materials Informatics ...

Showing results 21-40

Intern Computational Materials Science information

What does an intern in computational materials science do?

An Intern in Computational Materials Science assists in research and development by applying computational techniques to study and predict the properties and behaviors of materials. Typical tasks include running simulations, analyzing data, and working with software tools to model materials at the atomic or molecular level. Interns may collaborate with researchers to design experiments, interpret results, and contribute to scientific publications or reports. This role provides hands-on experience in both computational methods and materials science, helping to bridge theory and practical application.

What types of projects can an intern in computational materials science expect to work on during their internship?

As an Intern in Computational Materials Science, you can expect to engage in projects involving simulations of material properties, data analysis from computational experiments, and the development of models to predict material behavior. You may collaborate with researchers and senior scientists to support ongoing investigations or help optimize simulation workflows. These projects often require proficiency in programming languages such as Python or MATLAB and may involve the use of specialized software like VASP or LAMMPS. The experience provides a hands-on understanding of how computational methods contribute to advancing materials research and often includes opportunities to present your findings to the team.

What are the key skills and qualifications needed to thrive as an intern in computational materials science, and why are they important?

To thrive as an Intern in Computational Materials Science, you need a solid background in materials science, physics, or engineering, along with coursework in computational modeling and data analysis. Familiarity with programming languages like Python or MATLAB, experience with simulation software (such as VASP or LAMMPS), and knowledge of high-performance computing are typically required. Strong analytical thinking, attention to detail, and effective teamwork are important soft skills for success in collaborative research environments. These skills enable interns to contribute meaningfully to research projects, analyze complex materials data, and communicate findings clearly within multidisciplinary teams.

What is the difference between Intern Computational Materials Science vs Intern Materials Engineering?

AspectIntern Computational Materials ScienceIntern Materials Engineering
Required CredentialsUndergraduate or graduate in materials science, physics, or related fields; basic programming skillsUndergraduate or graduate in materials engineering, mechanical engineering, or related fields; foundational technical knowledge
Work EnvironmentResearch labs, computational modeling, data analysisDesign, testing, and development in labs or manufacturing settings
Industry UsageResearch institutions, tech companies, aerospace, academiaManufacturing firms, product development, construction

Intern Computational Materials Science focuses on computational modeling and simulations of materials properties, while Intern Materials Engineering emphasizes practical design, testing, and application of materials. Both roles require a background in materials-related fields but differ in their core activities and work environments.

More about Intern Computational Materials Science jobs

What cities are hiring for Intern Computational Materials Science jobs?

Cities with the most Intern Computational Materials Science job openings:

What are the most commonly searched types of Computational Materials Science jobs?

The most popular types of Computational Materials Science jobs are:

What states have the most Intern Computational Materials Science jobs?

States with the most job openings for Intern Computational Materials Science jobs include:

Infographic showing various Intern Computational Materials Science job openings in the United States as of August 2026, with employment types broken down into 11% Internship, 67% Full Time, and 22% Part Time. Highlights an 78% In-person, and 22% Remote job distribution.

Computational Materials Scientist - Postdoctoral Researcher

LLNL

Livermore, CA โ€ข On-site

$123K/yr

Full-time

Retirement

Re-posted 20 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 twoof 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 2026Best 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, wewill initiate a Federal background investigation to determine if youmeet 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 useand/or possession ofmobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area whereyou are not permitted to have a personal and/or laboratory mobile devicein your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.
Ifyou useamedical device, whichpairs with a mobile device,you must still follow the rules concerningthe mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities requireseparate 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.
CaliforniaPrivacy Notice
The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitlesjob 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 .