With recent advancements in smart sensors, Industrial Internet of Things (IIoT), artificial intelligence (AI), cyber-physical systems (CPS), and modeling and simulation, digital twins of manufacturing products, systems, and processes can be realized. However, their effectiveness depends heavily on user trust in the digital twin's outputs and decision recommendations. Trustworthiness in digital twins goes beyond model fidelity, it requires validated accuracy, quantified uncertainty, explainability, robustness, and secure, high-quality data. As digital twins evolve with their physical counterparts, maintaining trust over time becomes especially challenging due to system updates, sensor drift, and aging components. This research addresses the critical need for a formal approach to ensure ongoing trust in digital twin systems by developing a structured Verification, Validation, and Uncertainty Quantification (VVUQ) framework aligned with existing standards and integrated across the digital twin lifecycle. Such an approach would enhance the agility and flexibility of manufacturing systems, as well as the competitiveness of the US manufacturing base.

NIST’s Engineering Lab (EL) has an opportunity for highly qualified post-doctoral researchers to conduct advanced research in Verification, Validation, and Uncertainty Quantification (VVUQ) for Digital Twins. This position focuses on establishing scientific rigor and model credibility for next-generation digital twin technologies used in manufacturing, cyber-physical systems, and engineered systems. The selected applicant will support the development of measurement science, standards, and methods that enable trustworthy digital-twin applications.

Responsibilities

• Develop and apply methods for model verification, including numerical accuracy assessment, convergence analysis, and error quantification.
• Conduct model validation using experimental or operational datasets; design validation metrics and assess model credibility.
• Perform uncertainty quantification using techniques such as Monte Carlo simulation, Bayesian inference, global sensitivity analysis, and surrogate modeling.
• Integrate physics-based models with sensor data through data assimilation, state estimation, or probabilistic updating.
• Support research on digital-twin standards, reference architectures, and best practices.
• Publish research findings in peer-reviewed journals and present results at conferences.
• Collaborate with interdisciplinary teams, industry partners, and external stakeholders.

Minimum Qualifications

• Ph.D. in Mechanical Engineering, Aerospace Engineering, Systems Engineering, Applied Mathematics, Computer Science, or a related engineering/science field.
• Demonstrated expertise in modeling & simulation, computational mechanics, or dynamical system modeling.
• Experience in at least one of the following: model verification, model validation, uncertainty quantification, or sensitivity analysis.
• Strong programming skills (e.g., Python, MATLAB, C++).
• Ability to manage and analyze complex datasets.
• Strong written and oral communication skills.

Preferred Qualifications

• Familiarity with ASME V&V standards or emerging ISO/IEC/IEEE digital-twin standards.
• Experience with surrogate modeling, probabilistic modeling, Bayesian methods, or ML-assisted UQ.
• Experience collaborating with experimentalists or working with real-world data for model validation.
• Experience in developing or analyzing digital twins or cyber-physical systems.
• A record of publications demonstrating expertise in VVUQ or simulation science.

Impact

This position presents a unique opportunity to advance foundational research in digital-twin credibility and contribute to national efforts that support advanced manufacturing, infrastructure resilience, and trustworthy cyber-physical systems. The post-doc will collaborate with multidisciplinary experts and industry partners to develop the next generation of measurement science for digital twins.

For further information, please contact

Gordon Shao
gshao@nist.gov

About NIST

NIST is an agency of the Department of Commerce. Our mission is to promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology in ways that enhance economic security and improve our quality of life. NIST is the world's leader in creating critical measurement solutions and promoting equitable standards. Our efforts stimulate innovation, foster industrial competitiveness, and improve the quality of life. NIST is an organization with strong values, reflected both in our history and our current work. NIST leadership and staff uphold these values to ensure a high-performing environment that is safe and respectful of all.

About EL

Our vision is to enable the next generation of innovative and competitive manufacturing through advances in measurement science. EL research projects aim to speed development, adoption, and integration of leading-edge intelligent technologies to advance the U.S. manufacturing while making sure that new technologies are integrated in ways that address fundamental competitive factors, including productivity, agility, quality, and sustainability.

Through research that stretches the limits of measurement science and pushes the envelope of current measurement and test capabilities, EL will:

• Safely increase the versatility, autonomy, and rapid re-tasking of intelligent robots and automation technologies for smart manufacturing and cyber-physical systems applications;
• Enable real-time monitoring, control, and performance optimization of smart manufacturing systems in the factories of small, medium, and large companies;
• Enable rapid, agile, and cost-effective production of complex, first-to-market products through advanced manufacturing processes and equipment; and
• Facilitate straightforward integration of engineering information systems used in complex manufacturing and construction networks to improve product and process performance.

REFERENCES

1. Shao, G., & Helu, M. (2020). Framework for a digital twin in manufacturing: Scope and requirements. Manufacturing Letters, 24, 105–107.
2. Shao, G., Hightower, J., & Schindel, W. (2023). Credibility consideration for digital twins in manufacturing. Manufacturing Letters, 35, 24–28.
3. ISO/TC 184/SC 4. (2021). ISO 23247-1:2021 Automation systems and integration — Digital twin framework for manufacturing: Part 1: Overview and general principles. International Organization for Standardization Geneva Switzerland.

Digital Twin; Digital Thread; Manufacturing; Standards; Verification, Validation, and Uncertainty Quantification (VVUQ); AI; Modeling and Simulation; Trust

citizenship

Open to U.S. citizens

level

Open to Postdoctoral applicants