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Uncertainty Quantification Jobs (NOW HIRING)

Duke University

Postdoctoral Associate

Durham, NC · On-site

Are interested in large-scale simulation, uncertainty quantification, and model calibration * Are motivated to work on interdisciplinary scientific questions that combine social, behavioral ...

Duke University

Postdoctoral Associate

Durham, NC · On-site

Are interested in large-scale simulation, uncertainty quantification, and model calibration * Are motivated to work on interdisciplinary scientific questions that combine social, behavioral ...

Google

Senior Staff Research Scientist

Mountain View, CA · On-site

$116K - $148K/yr

Demonstrated track record in deep learning, epistemic/aleatoric uncertainty quantification, model calibration for decision-making, and out of distribution diagnosis and generalization. . * Strong ...

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Uncertainty Quantification information

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$30K

$69.5K

$121K

How much do uncertainty quantification jobs pay per year?

As of Sep 14, 2026, the average yearly pay for uncertainty quantification in the United States is $69,454.00, according to ZipRecruiter salary data. Most workers in this role earn between $45,000.00 and $83,000.00 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.

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Uncertainty Quantification jobs near you
Infographic showing various Uncertainty Quantification job openings in the United States as of September 2026, with employment types broken down into 78% Full Time, 11% Part Time, and 11% Nights. Highlights an 78% In-person, and 22% Remote job distribution, with an average salary of $69,454 per year, or $33.4 per hour.

NIST PREP Postdoc Associate in Statistical Analysis and Tool Development for the NIST GenAI Evaluati

Gaithersburg, MD • On-site

Southeastern Universities Research Association
11 - 50 employees
Learn more about Southeastern Universities Research Associationexternal

$100K/yr

Full-time

Re-posted 16 days ago

Job description

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research.
Research Title: Statistical Analysis and Tool Development for the NIST GenAI Evaluation Program
The work will entail: This position involves statistical analysis and analysis tool development supporting the NIST GenAI evaluation series (https://ai-challenges.nist.gov/genai) within NIST's Information Technology Laboratory. The primary project is a Testing and Evaluation (T&E) framework for generative AI watermarking, where the associate will lead the statistical analysis of evaluation results and develop the statistical engine behind NIST-CARAT (Calibrated Risk Assessment Tool for authentication technologies), an interactive tool enabling policymakers to explore the empirical performance consequences of compliance-threshold choices. This work centers on characterizing the tradeoff between content quality and watermark resilience under routine image handling, using rigorous detection-performance analysis, calibration assessment, uncertainty quantification, and Bayes-risk estimation to produce internationally defensible threshold claims.
Beyond this project, the associate will contribute statistical and analytical support across the wider NIST GenAI evaluation portfolio, which spans the evaluation of generative AI technologies across multiple modalities (text, voice, image, video, and code). This includes experimental design, metric development, analysis of evaluation outputs, and the development of reproducible analysis pipelines and interactive reporting tools. The associate will actively participate in NIST measurement science and contribute to cutting-edge research and evaluation in generative AI.
U.S. Citizen Preferred
Key responsibilities will include but are not limited to:
  • Analyzing generative AI evaluation results using detection and classification performance methods (ROC, partial AUC, equal error rate, Brier score) and Bayes-risk characterization
  • Performing uncertainty quantification and calibration assessment to support defensible threshold and compliance claims
  • Developing the statistical backend of NIST-CARAT, including the models mapping compliance-threshold choices to expected operational consequences
  • Designing and implementing reproducible analysis pipelines from raw evaluation outputs through to estimates with calibrated uncertainty
  • Building interactive analysis and reporting tools (e.g., dashboards, interactive plots) to communicate evaluation results to technical and policy audiences
  • Processing large evaluation datasets, including work in GPU-accelerated, high-performance computing environments
  • Exploring data through descriptive statistics and graphical displays
  • Contributing to internal reports, workshop white papers, and submissions to international standards bodies (e.g., ISO/IEC JTC 1/SC 42 and SC 29)
  • Explaining statistical and metrological concepts to non-statisticians, including policy and standards stakeholders

Qualifications
  • A Ph.D. in statistics, biostatistics or a closely related quantitative field
  • Mastery of statistical analysis methods, including experimental design, detection/classification evaluation (ROC, partial AUC, EER, Brier score), calibration, uncertainty quantification, and Bayes-risk characterization
  • Experience with Bayesian modeling and Monte Carlo / Markov Chain Monte Carlo methods
  • Proficiency in a statistical computing and scripting language (e.g., R, Python) and in shell scripting, with version-controlled, reproducible analysis workflows
  • Experience building interactive analysis tools and dashboards (e.g., R Shiny, interactive plotting libraries, Jupyter notebooks)
  • Familiarity with generative AI tools, including large language models, and with AI test and evaluation
  • Experience with high-performance or GPU-accelerated computing environments, and with containerization and workflow tooling (e.g., Docker, Argo Workflows) in a data-analysis context
  • Strong communication skills in speaking, writing, and graphical display, including the ability to explain statistical concepts to non-statisticians

Privacy Act StatementAuthority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated.
SURA is an Equal Opportunity Employer. We believe that no one should be discriminated against because of their differences, such as age, disability, ethnicity, gender, gender identity and expression, religion, or sexual orientation. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status, or any other basis as protected by federal, state, or local law.
PREP0004912

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