Postdoctoral Research Associate, Systems and Industrial Engineering

The Department of Systems and Industrial Engineering seeks a Postdoctoral Research Associate to support research at the intersection of systems engineering and digital engineering, with an emphasis on advancing methods, tools, and architectures that enable modern engineering practice. The individual will contribute original scholarship and applied research, developing prototypes and capabilities that strengthen model-based and data-driven approaches. The position values interdisciplinary thinking, particularly where software development, emerging AI-enabled techniques, and systems modeling intersect. Responsibilities include publishing and presenting research results while helping shape and mature a digital engineering sandbox environment.

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

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• Author and co-author peer-reviewed journal papers targeting venues such as Systems Engineering (Wiley/INCOSE), SIMULATION (SCS/SAGE), Applied Ontology (IOS Press), and relevant IEEE/ACM journals.
• Prepare and present conference papers at CSER, INCOSE IS, CESUN, and similar venues.
• Contribute to technical reports and sponsor deliverables as needed.
• Support the design and implementation of a digital engineering sandbox environment for capability prototyping, training, and experimentation.
• Integrate emerging SE tooling (e.g., AI-assisted workflows, ontology-backed reasoning, requirements co-pilots) into the sandbox.
• Ensure the sandbox supports controlled experimentation and repeatable demonstrations of SE capabilities.
• Design, implement, and evaluate next-generation SE capabilities such as AI-augmented requirements engineering, automated conflict detection, model-based review support, and change impact analysis.
• Advance selected capabilities from early maturity toward cross-context application using the project’s assessment framework.
• Prototype and test novel capability concepts informed by the transformation roadmap and sponsor priorities.
• Conduct systematic literature reviews on digital engineering transformation, AI-augmented systems engineering, readiness assessment frameworks, and formal methods for SE.
• Monitor and synthesize emerging SE capabilities.
• Maintain a living literature database supporting project deliverables and journal submissions.
• Design and execute controlled experiments, case studies, interviews, or surveys to generate rigorous evidence on SE capability effectiveness.
• Collect and analyze data from sponsors and stakeholders on adoption barriers, governance gaps, and capability value.
• Engage with professional organizations (e.g., INCOSE) to solicit expert feedback on the transformation roadmap framework and assessment methodology.
• Mentor and supervise undergraduate research assistants working on project tasks.
• Define scoped research tasks appropriate for undergraduate contribution (literature coding, data collection, prototype testing, documentation).
• Review student work products and support their professional development (conference presentations, writing skills, research methods).

Knowledge, Skills, and Abilities

• Knowledge of systems engineering principles, including model-based systems engineering (MBSE).
• Knowledge of digital engineering concepts, tools, and transformation initiatives.
• Skilled in utilizing Python.

• Ph.D. in Systems Engineering, Computer Science, Industrial Engineering, or a closely related field. The selected candidate must have a conferred Ph.D. upon hire. 
• Experience publishing peer-reviewed journals and conference papers (e.g., IEEE, ACM, or comparable venues).
• Experience designing and implementing research prototypes or software tools.

• Experience with model-based systems engineering tools (e.g., SysML, Cameo, MagicDraw, Capella).
• Demonstrate familiarity with digital engineering ecosystems and sandbox/testbed environments.
• Experience with AI/ML techniques applied to systems engineering problems (e.g., requirements analysis, reasoning, automation).
• Experience in ontology engineering, knowledge graphs, or semantic technologies.
• Experience integrating heterogeneous tools and workflows (e.g., APIs, co-simulation, digital threads).