Postdoctoral Research Associate - Data Science for Advanced Manufacturing
Battelle
Summary
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.
Job description
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.
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
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
- 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.
Requirements
- 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
- Applicants cannot have received their Ph.D. more than five years prior to the date of application
- Must complete all degree requirements before starting their appointment
- Visa sponsorship is not available for this position
- 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
- Must be able to obtain and maintain a federal Personal Identity Verification (PIV) card
- Must successfully complete and pass a Federal Tier 1 background check investigation
Benefits
- Medical Plans
- Retirement Plans
- Flexible Work Hours
- Prescription Drug Plan
- Dental Plan
- Vision Plan
- 401(k) Retirement Plan
- Contributory Pension Plan
- Life Insurance
- Disability Benefits
- Generous Vacation and Holidays
- Parental Leave
- Legal Insurance
- Identity Theft Protection
- Employee Assistance Plan
- Flexible Spending Accounts
- Health Savings Accounts
- Wellness Programs
- Educational Assistance
- Relocation Assistance
- Employee Discounts
Company overview
Battelle is a leading nonprofit research and development organization headquartered in Columbus, Ohio, with a rich history dating back to 1929. As one of the world's largest independent research institutions, Battelle conducts scientific research and technology development across multiple sectors including national security, health and life sciences, energy, and environmental sustainability. The organization manages numerous national laboratories for the U.S. Department of Energy, including Pacific Northwest National Laboratory, and provides critical research services to government agencies, commercial clients, and academic institutions. Battelle's work spans from fundamental scientific discovery to applied technology development, with significant contributions in areas such as biomedical research, materials science, cybersecurity, and advanced manufacturing. The organization plays a vital role in translating scientific discoveries into practical applications that address complex global challenges while maintaining a strong commitment to public service and scientific integrity.