1

Uncertainty Quantification Jobs in California (NOW HIRING)

... Uncertainty Quantification (VVUQ), and workflow automation. These positions are in the Computational Engineering Division (CED, within the Engineering Directorate). Depending on your assignment ...

... Uncertainty Quantification (VVUQ), and workflow automation. These positions are in the Computational Engineering Division (CED, within the Engineering Directorate). Depending on your assignment ...

... Uncertainty Quantification (VVUQ), and workflow automation. These positions are in the Computational Engineering Division (CED, within the Engineering Directorate). Depending on your assignment ...

Experience with verification, validation, and uncertainty quantification for structural simulations as per ASME V&V 10, 20, and 40. * Experience of writing detailed technical reports. * Effective ...

Experience with DOE, optimization, uncertainty quantification, or statistical analysis. Experience with simulation automation, surrogate models, or ML-based simulation acceleration. Experience with ...

Experience with DOE, optimization, uncertainty quantification, or statistical analysis. Experience with simulation automation, surrogate models, or ML-based simulation acceleration. Experience with ...

Develop robust multi-objective optimization and uncertainty-quantification workflows to ensure AI-generated designs are manufacturable, robust to variation, and compatible with downstream yield ...

... uncertainty quantification, and scenario simulation. • Apply advanced statistical and ML techniques to model customer behavior, product adoption, revenue dynamics, and cost trends. • Drive ...

Knowledge of simulation model validation, model calibration, and uncertainty quantification methods. At Archer, we aim to attract, retain, and motivate talent with the skills and leadership needed to ...

Knowledge of simulation model validation, model calibration, and uncertainty quantification methods. At Archer, we aim to attract, retain, and motivate talent with the skills and leadership needed to ...

Showing results 21-40

Uncertainty Quantification information

See California salary details

$29.6K

$68.5K

$119.4K

How much do uncertainty quantification jobs pay per year?

As of Aug 22, 2026, the average yearly pay for uncertainty quantification in California is $68,544.00, according to ZipRecruiter salary data. Most workers in this role earn between $44,400.00 and $81,900.00 per year, depending on experience, location, and employer.

What is uncertainty quantification?

Uncertainty Quantification (UQ) is the science of quantifying, managing, and reducing uncertainties in computational models and real-world systems. It involves identifying sources of uncertainty in data, model parameters, and algorithms, then using statistical and mathematical methods to assess their impact on model predictions. UQ is essential in fields like engineering, finance, and environmental science to ensure that predictions and decisions are robust and reliable. Practitioners use techniques such as sensitivity analysis, probabilistic modeling, and Monte Carlo simulations to quantify and analyze uncertainties.

What are the key skills and qualifications needed to thrive as an uncertainty quantification specialist?

To thrive as an Uncertainty Quantification Specialist, you need a strong background in applied mathematics, statistics, and computational modeling, often supported by an advanced degree in a quantitative field. Familiarity with programming languages such as Python or MATLAB, and experience with simulation tools and statistical analysis software, are typically required. Strong problem-solving skills, attention to detail, and effective communication help professionals convey complex concepts to interdisciplinary teams. These skills are crucial for accurately assessing risks, making data-driven decisions, and improving the reliability of models in engineering, finance, or scientific research.

What are some common challenges faced by professionals in uncertainty quantification when working on multidisciplinary teams?

Professionals in Uncertainty Quantification (UQ) often collaborate with experts from fields like engineering, data science, and physics. A common challenge is communicating complex statistical concepts in an accessible way to team members without a quantitative background. Additionally, integrating uncertainty models into existing workflows and ensuring that all stakeholders understand how uncertainty impacts decision-making can be demanding. Effective UQ professionals are proactive in facilitating clear communication and tailoring their approach to fit the needs of diverse teams.

What is the difference between Uncertainty Quantification vs Data Scientist?

AspectUncertainty QuantificationData Scientist
Required credentialsAdvanced degrees in engineering, mathematics, or statisticsDegree in computer science, statistics, or related fields
Work environmentResearch labs, engineering firms, simulation-based industriesTech companies, finance, healthcare, and marketing
Industry usageEngineering, aerospace, manufacturing, scientific researchBusiness analytics, product development, predictive modeling

Uncertainty Quantification focuses on assessing and reducing uncertainty in models and simulations, often requiring advanced mathematical skills. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles involve statistics and data analysis, Uncertainty Quantification is more specialized in modeling uncertainties in engineering and scientific contexts, whereas Data Scientists work across diverse industries with a broader focus on data-driven insights.

What are popular job titles related to Uncertainty Quantification jobs in California?

For Uncertainty Quantification jobs in California, the most frequently searched job titles are:

What job categories do people searching Uncertainty Quantification jobs in California look for?

The top searched job categories for Uncertainty Quantification jobs in California are:

What cities in California are hiring for Uncertainty Quantification jobs?

Cities in California with the most Uncertainty Quantification job openings:

Infographic showing various Uncertainty Quantification job openings in California as of August 2026, with employment types broken down into 78% Full Time, 20% Part Time, and 2% Contract. Highlights an 78% Physical, 3% Hybrid, and 19% Remote job distribution, with an average salary of $68,544 per year, or $33 per hour.

Computational Materials Scientist - Postdoctoral Researcher

LLNL

Livermore, CA • On-site

$9.8K/wk

Full-time

Retirement

Re-posted 8 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.
Videos To Watch
https://www.youtube.com/watch?v=ITnv867DReU