Postdoctoral Research Associate - Data Science for Advanced Manufacturing

UT-Battelle; Oak Ridge (TN); On-site; USD 60,000 - 85,000; Full time

Job summary

UT-Battelle's Oak Ridge National Laboratory is seeking Postdoctoral Research Associates in Data Science for Advanced Manufacturing to develop data-driven manufacturing systems that integrate AI, real-time sensing, and digital twins.

You will work within the Manufacturing Systems Analytics group to create scalable AI-ready datasets, real-time monitoring, predictive modeling, and process optimization for diverse manufacturing modalities.

Responsibilities

• Real-time quality monitoring and control of manufacturing processes.
• Understanding relationships between manufacturing intent, machine behavior, and part performance.

Job description

Overview

We are accepting applications for Postdoctoral Research Associate positions in Data Science for Advanced Manufacturing that will focus on the development of next-generation, data-driven manufacturing systems that integrate artificial intelligence, real-time sensing, and digital twins to transform how critical components are designed, produced, and qualified. The selected candidates will conduct research in data science and AI to develop scalable, deployable methodologies to assess and to improve manufacturing quality, efficiency, and certification readiness. This position resides in the Manufacturing Systems Analytics group in the Digital and Secure Manufacturing Section, Manufacturing Science Division, Energy Science and Technology Directorate (ESTD) at Oak Ridge National Laboratory (ORNL).

Requisition Id 16779

You will work at the MDF to advance digital manufacturing technologies and to accelerate their deployment to industry and national scale applications. The MDF hosts a diverse set of advanced manufacturing systems - including powder bed, directed energy deposition, machining, polymer, and convergent manufacturing systems - used to produce critical components from advanced materials.

These systems are instrumented and connected through a unified digital thread platform that captures multimodal, high-frequency data across the full manufacturing lifecycle, from process execution to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive modeling, and process optimization.

In this role, you will leverage large-scale, heterogeneous datasets to develop and deploy AI-driven methods for:

• Real-time quality monitoring and control of manufacturing processes
• Understanding relationships between manufacturing intent, machine behavior, and part performance