Postdoctoral Research Associate, Systems and Industrial Engineering

The Department of Systems and Industrial Engineering at the University of Arizona invites applications for a Postdoctoral Research Associate to join our research group working at the intersection of advanced manufacturing, computational modeling, and machine learning. This position offers an opportunity to contribute to cutting-edge research in design automation, manufacturability analysis, and data-driven approaches for additive and conventional manufacturing processes.

Outstanding UA benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; UA/ASU/NAU tuition reduction for the employee and qualified family members; access to UA recreation and cultural activities; and more!

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• Developing automated manufacturability analysis methods for additive and conventional manufacturing processes.
• Applying machine learning techniques to 3D engineering data (CAD models, meshes, voxels, etc.).
• Leveraging computational geometry methods for advanced manufacturing applications.
• Advancing topology optimization methods for lightweight and high-performance designs.
• Collaborating with faculty, graduate students, and industry partners to publish high-impact research and develop tools with real-world applications.

Knowledge, Skills, And Abilities

• Strong communication skills and ability to work in an interdisciplinary team.
• Excellent programming skills and proficiency in a programming language (e.g., C++, Python, MATLAB) and modern machine learning packages.
• In-depth theoretical and practical knowledge in additive manufacturing.
• Ability to work independently and collaboratively within a team environment.
• Strong work ethic, effective communication skills, and a collaborative, team-oriented mindset.

• Ph.D. in Systems and Industrial Engineering, Mechanical Engineering, Computer Science, or a related field. The selected candidate must have a conferred Ph.D. upon hire.

• Strong background in computational methods, CAD/geometry processing, or machine learning.
• Demonstrated research record with publications in relevant fields.
• Strong background in machine learning and manufacturing.
• Experience with additive manufacturing or conventional manufacturing processes.
• Familiarity with geometry processing libraries and CAD/mesh data representations.
• Background in optimization methods, particularly topology optimization.
• Proven research record, including peer-reviewed publications.