Oak Ridge National Laboratory
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Postdoctoral Research Associate in Data Science for Advanced Manufacturing Oak Ridge National Laboratory in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Full funding availableCountry
United States
University
Oak Ridge National Laboratory

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About this position
Oak Ridge National Laboratory (ORNL) is accepting applications for a Postdoctoral Research Associate in Data Science for Advanced Manufacturing in Oak Ridge, Tennessee, United States.
The position sits in the Manufacturing Systems Analytics group within the Digital and Secure Manufacturing Section, Manufacturing Science Division, Energy Science and Technology Directorate. The research focuses on next-generation, data-driven manufacturing systems that combine artificial intelligence, real-time sensing, digital twins, multimodal data, and advanced analytics to improve manufacturing quality, efficiency, certification readiness, and process optimization.
Selected candidates will work with large-scale heterogeneous datasets from advanced manufacturing systems, including powder bed, directed energy deposition, machining, polymer, and convergent manufacturing platforms. The role involves developing imaging and sensing methods, modular data-processing workflows, machine learning and statistical models, anomaly detection, predictive modeling, and decision-support tools for real-time and edge deployment.
Required background includes a PhD in mechanical engineering, materials science, electrical engineering, computer engineering, computer science, data science, applied mathematics, or a closely related field. Applicants should have experience with multimodal data acquisition, manufacturing data analytics, statistical modeling, and machine learning, along with strong Python skills and familiarity with common ML libraries such as NumPy, Pandas, SciPy, scikit-learn, PyTorch, and TensorFlow.
Preferred experience includes manufacturing and sensor data, streaming or time-series systems, edge AI, data pipelines, multimodal datasets, workflow automation, experimental design, uncertainty quantification, scientific machine learning, and digital twin methodologies. The post also requires strong communication skills, the ability to work in multidisciplinary teams, and eligibility for export-controlled access.
This is a postdoctoral appointment for up to 24 months, with possible extension depending on performance and funding. Visa sponsorship is not available. Applicants must have completed the PhD within the last five years and must finish all degree requirements before starting.
To apply, use the ORNL jobs portal and submit the required application materials. Three letters of reference are requested. If needed, applicants can contact [email protected] for help with the application process.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
How to apply
Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.
More information can be found here
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