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Uncertainty Quantification Jobs (NOW HIRING)

Duke University

Postdoctoral Associate

Durham, NC

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 ...

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Uncertainty Quantification information

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$30K

$69.5K

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How much do uncertainty quantification jobs pay per year?

As of Aug 22, 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 August 2026, with employment types broken down into 73% Full Time, 25% Part Time, and 2% Contract. Highlights an 78% Physical, 3% Hybrid, and 19% Remote job distribution, with an average salary of $69,454 per year, or $33.4 per hour.

Research Scientist, Quantum Chemistry

Dayhoff Labs

Cambridge, MA • On-site

Full-time

Posted yesterday

New

Job description

About us
We're reverse-engineering the origin of life - one of the great unsolved problems in science, and one we think AI finally makes tractable. We believe that understanding this transition, from geochemistry to biochemistry, will let us orchestrate molecular networks and build systems that are more capable, adaptive, efficient, and intelligent.
If we succeed, the applications are vast: from catalysis and green synthesis to ab initio synthetic biology and programmable matter. Understanding and harnessing these processes could let ten billion of us thrive on this planet, and let us dream that diverse life keeps evolving and thriving beyond it.
We're a small, diverse team of AI engineers, computational scientists, and bench scientists. We hold ourselves to the rigor of a research institute, but we ship like an engineering firm. Global team, HQs in Cambridge, MA and London, UK.
The role
You'll turn quantum-chemistry calculations into kinetic datasets and mechanistic insight our ML models can actually train on. You'll study reaction mechanisms across homogeneous, heterogeneous, and enzymatic systems, and build the protocols that make that data reliable at scale.
What you'll do
  • Run DFT and post-HF calculations to study kinetics and mechanism, primarily in homogeneous catalysis
  • Build and benchmark reproducible protocols for kinetic data generation, with real uncertainty quantification
  • Design kinetic datasets for ML training and validation, and set data-quality standards with ML collaborators
  • Extend these methods systematically across catalytic systems and reaction conditions

Essential experience
  • PhD in computational or theoretical chemistry with a catalysis focus, and first-author papers on catalytic mechanisms
  • Fluency with a production quantum-chemistry package (Gaussian, ORCA, or similar)
  • Sound DFT judgment for transition-metal systems: functional choice, basis sets, dispersion corrections
  • Hands-on kinetics: transition-state location, IRC, rate constants, free-energy and thermodynamic analysis
  • Python and the computational-chemistry stack (ASE, cclib, RDKit)

Highly preferred
  • First-author work in homogeneous-catalysis kinetics
  • Heterogeneous (periodic DFT, surfaces, adsorption) or enzyme catalysis
  • Advanced methods for hard systems: DLPNO-CCSD(T), CASPT2, multireference approaches
  • High-throughput workflows, HPC, and automation
  • Uncertainty quantification and protocol benchmarking
  • Dataset design and prior collaboration with ML teams

Logistics
Compensation is highly competitive. We're also able to sponsor visas for the right candidate.

Apply