Postdoctoral Research Associate, Systems and Industrial Engineering

UNIVERSITY OF ARIZONA; Tucson (AZ); On-site; USD 60,000 - 80,000; Full time

Benefits offered by this job

Health insurance

Paid vacation

Tuition reduction

Access to cultural activities

Job summary

A prominent research institution seeks a Postdoctoral Research Associate in Systems and Industrial Engineering. This role involves supporting research at the intersection of systems and digital engineering, focusing on original scholarship, prototype development, and integration of AI techniques. The ideal candidate will hold a Ph.D. and possess experience in publishing and implementing research prototypes. Comprehensive benefits include health insurance, tuition reduction, and professional development opportunities.

Qualifications

• Ph.D. must be conferred upon hire.
• Experience publishing peer-reviewed journals and conference papers.
• Experience designing and implementing research prototypes or software tools.

Responsibilities

• Author and co-author peer-reviewed journals.
• Prepare and present conference papers.
• Support the design of a digital engineering sandbox.

Skills

Knowledge of systems engineering principles

Knowledge of digital engineering concepts

Utilizing Python

Education

Ph.D. in Systems Engineering, Computer Science, Industrial Engineering, or related field

Tools

Model-based systems engineering tools (SysML, Cameo, MagicDraw, Capella)

Job description

Postdoctoral Research Associate, Systems and Industrial Engineering

Postdoctoral Research Associate, Systems and Industrial Engineering Posting Number req25668 Department Systems and Industrial Engr Department Website Link https://sie.engineering.arizona.edu/ Location Tucson Campus Address 1127 E. James E. Rogers Way, Tucson, AZ 85721 USA

Position Highlights

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.

Benefits

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!

Duties & Responsibilities

• 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.

Minimum Qualifications

• 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.

Preferred Qualifications

• 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).