Postdoctoral fell; PREP

PREP Research Associate – CHIPS Funded Project.

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience (PREP) program. The NIST PREP program allows NIST laboratories to partner with academic institutions on research projects of mutual interest. Employees in this role will perform technical work underpinning scientific research of the collaboration.

Closing material loops to strengthen US semiconductor supply chains

The Life Cycle Engineering Group in NIST’s Engineering Lab seeks a researcher in operations research and industrial engineering. The project focuses on maximizing the value of U.S. microelectronics and semiconductors through circular economy principles. The goal is to develop forecasting methods that establish and validate circular pathways for microelectronic recovery, delivering decision‑support tools that strengthen domestic semiconductor manufacturing capabilities and improve supply‑chain resilience.

• Conduct a literature survey and collect data on the state‑of‑the‑art microchip manufacturing processes and chip product value chain.
• Develop an in‑depth understanding of the microchip market, highlighting strengths and limitations of U.S. chip‑making.
• Create high‑fidelity simulation models across product life cycles to quantify material stocks and flows, and to assess risks and uncertainties in the domestic chip manufacturing sector.
• Propose quantitative evaluation metrics to effectively maximize microchip value.
• Regularly synthesize results and disseminate findings through NIST program partners, special reports, high‑impact journal publications, and presentations at technical conferences.

Ph.D. or M.S. in a relevant field with a record of peer‑reviewed publications is preferred. US citizenship is required.

• Understanding of micro/nano fabrication processes for chips and first‑principle modeling.
• Systems thinking, supply‑chain modeling, and knowledge of integrated production systems.
• Data analysis and visualization using Python and/or MATLAB.
• Predictive modeling and machine‑learning techniques, including dimensionality reduction, regression, and classification (ANOVA, non‑parametric methods).
• Stochastic modeling of manufacturing systems.
• Principles of integrated production systems, material handling, flow, and scheduling.

Please upload the following with your application:

• CV/Resume (limit to 3 pages, include a valid email address; do not include self‑portraits, phone number, home address/country, citizenship status, languages spoken, or gender).
• Self‑portrait (if required).
• Phone number (if required).
• Home address/country (if required).
• Citizenship status (if required).
• Languages spoken.
• Sex/Gender.

Applicants will be required to sign a Non‑Disclosure Agreement prior to commencing work.