Associate R&D Staff Member in Data Science for Advanced Manufacturing (Temporary
Oak Ridge National Laboratory, Knoxville, TN, United States
Overview
We are seeking an Associate R&D Staff Member in Data Science for Advanced Manufacturing who 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 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).
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.
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.
Major Duties/Responsibilities
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.
Design and implement scalable data engineering pipelines for ingestion, transformation, validation, and curation of manufacturing data, enabling high-quality, AI-ready datasets. Develop, integrate, and evaluate AI/ML models for anomaly detection, predictive modeling, process optimization, and automated decision support, including real‑time and edge deployment.
Contribute to the development of software tools and workflows for processing and analyzing manufacturing and characterization data.
Develop and integrate imaging and sensing systems for data collection and monitoring.
Develop modular, extensible workflows (e.g., service-oriented or agent-based architectures) to orchestrate data processing, simulation, and decision-making.
Publish research results in peer-reviewed journals and present findings at scientific conferences.
Mentor students and junior staff.
Collaborate with multidisciplinary teams to provide computational and analytical expertise across projects.
Support broader research and development activities within the MDF.
Contribute to proposals, publications, and cross‑organizational collaborations to advance digital manufacturing research.
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.
Basic Qualifications
Ph.D. in mechanical engineering, material science, electrical engineering, computer engineering, computer science, data science, applied mathematics, or a closely related field.
Demonstrated experience applying data analytics, statistical modeling, and machine learning to real‑world datasets.
Experience conceiving and executing research and development projects.
Proficiency in Python and common data science and machine learning libraries (NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow).
Experience developing and deploying machine learning or deep learning models.
Experience building and maintaining data processing pipelines for structured and unstructured data.
Familiarity with high-performance computing, cloud environments, or distributed data systems.
Familiarity with uncertainty quantification methods in AI/ML.
Ability to present complex results to multidisciplinary teams.
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 or streaming data systems and edge AI deployment.
Experience with multimodal datasets (imaging, time-series, 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 capable of independent work and creative collaboration across the laboratory.
Ability to function well in a fast-paced research environment, set priorities, and adapt to changing needs.
Special Requirements
Visa sponsorship is not available for this position.
This is a temporary 24-month position. Appointments may be extended based on performance and funding availability.
Three letters of reference are required.
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.
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We are seeking an Associate R&D Staff Member in Data Science for Advanced Manufacturing who 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 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).
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.
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.
Major Duties/Responsibilities
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.
Design and implement scalable data engineering pipelines for ingestion, transformation, validation, and curation of manufacturing data, enabling high-quality, AI-ready datasets. Develop, integrate, and evaluate AI/ML models for anomaly detection, predictive modeling, process optimization, and automated decision support, including real‑time and edge deployment.
Contribute to the development of software tools and workflows for processing and analyzing manufacturing and characterization data.
Develop and integrate imaging and sensing systems for data collection and monitoring.
Develop modular, extensible workflows (e.g., service-oriented or agent-based architectures) to orchestrate data processing, simulation, and decision-making.
Publish research results in peer-reviewed journals and present findings at scientific conferences.
Mentor students and junior staff.
Collaborate with multidisciplinary teams to provide computational and analytical expertise across projects.
Support broader research and development activities within the MDF.
Contribute to proposals, publications, and cross‑organizational collaborations to advance digital manufacturing research.
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.
Basic Qualifications
Ph.D. in mechanical engineering, material science, electrical engineering, computer engineering, computer science, data science, applied mathematics, or a closely related field.
Demonstrated experience applying data analytics, statistical modeling, and machine learning to real‑world datasets.
Experience conceiving and executing research and development projects.
Proficiency in Python and common data science and machine learning libraries (NumPy, Pandas, SciPy, scikit-learn, PyTorch, TensorFlow).
Experience developing and deploying machine learning or deep learning models.
Experience building and maintaining data processing pipelines for structured and unstructured data.
Familiarity with high-performance computing, cloud environments, or distributed data systems.
Familiarity with uncertainty quantification methods in AI/ML.
Ability to present complex results to multidisciplinary teams.
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 or streaming data systems and edge AI deployment.
Experience with multimodal datasets (imaging, time-series, 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 capable of independent work and creative collaboration across the laboratory.
Ability to function well in a fast-paced research environment, set priorities, and adapt to changing needs.
Special Requirements
Visa sponsorship is not available for this position.
This is a temporary 24-month position. Appointments may be extended based on performance and funding availability.
Three letters of reference are required.
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.
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