Associate R&D Staff Member in Data Science for Advanced Manufacturing (Temporary)

About the role

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.

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.

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 (e.g., 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.

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 (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.