Postdoctoral Research Associate - Data Science for Advanced Manufacturing

Oak Ridge National Laboratory; Oak Ridge (TN); On-site; USD 65,000 - 90,000; Full time

Job summary

Oak Ridge National Laboratory (ORNL) is seeking Postdoctoral Research Associates in Data Science for Advanced Manufacturing. You will develop AI-driven, data-centric methods and digital twins to transform manufacturing quality and efficiency across diverse systems.

Responsibilities include real-time monitoring, model development, and deployment at scale, with opportunities to publish and contribute to high-impact programs.

Qualifications

• PhD in a required field as listed.
• Experience with multimodal data acquisition and ML in manufacturing.
• Proficiency in Python and ML libraries (NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow).
• Experience developing and deploying ML models and edge AI deployments.
• Excellent communication and ability to work in multidisciplinary teams.

Responsibilities

• Real-time quality monitoring and control of manufacturing processes.
• Understand relationships between manufacturing intent, machine behavior, and part performance.
• Optimize manufacturing processes for throughput, reliability, and quality.
• Develop and deploy data analytics, ML, and statistical methods for multimodal datasets.
• Develop, integrate, and evaluate AI/ML models for anomaly detection, predictive modeling, and process optimization.

Skills

Python programming

Data science

Machine learning

Communication

Multidisciplinary collaboration

Edge AI deployment

Education

PhD in engineering/science

Tools

Python ML libraries

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).

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.

Responsibilities

1. Real-time quality monitoring and control of manufacturing processes
2. Understanding relationships between manufacturing intent, machine behavior, and part performance
3. Optimization of manufacturing processes for improved throughput, reliability, and quality

You will contribute to the development of integrated data and AI workflows that span data acquisition, modeling, and decision-making, including deployment at the edge and across distributed systems. You will have access to extensive experimental and computational resources and will be expected to publish research, present results, and contribute to high-impact programs. With over 100 manufacturing systems at the MDF, this role offers the opportunity to work on diverse, high-impact problems and to shape the future of intelligent manufacturing.

Major Duties/Responsibilities

• Develop and integrate imaging and other sensing modalities for data collection and monitoring in manufacturing environment
• Develop modular, extensible workflows for data processing
• Develop and deploy data analytics, machine learning, and statistical modeling methods for multimodal manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and digital twin data.
• Develop, integrate, and evaluate AI/ML models for anomaly detection, predictive modeling, process optimization, and automated decision support, including real-time and edge deployment
• Collaborate with multidisciplinary teams to provide sensing, computational, and analytical expertise across projects
• Support broader research and development activities within the MDF
• Deliver ORNL's mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace - in how we treat one another, work together, and measure success.

Basic Qualifications

• PhD. in mechanical engineering, material science, electrical engineering, computer engineering, computer science, data science, applied mathematics, or a closely related field
• Demonstrated experience with multimodal data acquisition, data analytics, statistical modeling, and machine learning in manufacturing environment.
• Proficiency in Python and common data science and machine-learning libraries (e.g., NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow)
• Experience developing and deploying machine learning or deep learning models
• Ability to present complex results to multidisciplinary teams, including engineering, scientific, and operational stakeholders
• Ability to work effectively in a dynamic, collaborative research environment
• Excellent verbal and written communication skills

Preferred Qualifications

• Experience working with manufacturing, materials, and sensor data
• Experience with real-time, time-series or streaming data systems and edge AI deployment
• Experience building and maintaining data processing pipelines for structured and unstructured data
• Experience with multimodal datasets (e.g., imaging, time-series, and process data)
• Experience with API-based data services, workflow automation, or integration of analytics into production systems
• Knowledge of experimental design, uncertainty quantification, scientific machine learning, or digital twin methodologies
• Experience collaborating across national laboratories, academia, or industry in multidisciplinary teams
• Excellent written and oral communication skills.
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory.
• Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever-changing needs.

Special Requirements

• Visa sponsorship: Visa sponsorship is not available for this position.
• Export control: This position requires access to technology that is subject to export control requirements. Successful candidates must be qualified for such access without an export control license.

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required. Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.

To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

Equal Opportunity

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply. UT‑Battelle is an E‑Verify employer.