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How much do systems engineering postdoc jobs pay per year?

As of Sep 8, 2026, the average yearly pay for systems engineering postdoc in the United States is $142,070.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,000.00 and $173,000.00 per year, depending on experience, location, and employer.

What is a systems engineering postdoc?

A Systems Engineering Postdoc is a researcher who has recently earned their PhD and conducts advanced research in systems engineering, often at a university or research institution. Their work typically involves developing, modeling, and optimizing complex engineering systems, integrating multiple disciplines such as software, hardware, and process engineering. Postdocs may lead independent projects, collaborate with faculty and industry partners, and publish their findings in academic journals. This role is designed to deepen expertise, contribute to scientific knowledge, and prepare for careers in academia, industry, or government research.

What types of projects and collaborations can a systems engineering postdoc typically expect to be involved with?

As a Systems Engineering Postdoc, you will often work on interdisciplinary research projects that bridge engineering, computer science, and applied mathematics. Collaboration is common with faculty, other postdocs, graduate students, and sometimes industry partners, depending on the project's scope. You may contribute to designing and optimizing complex systems, developing new methodologies, publishing research findings, and helping to secure grant funding. This role offers opportunities to mentor students, present at conferences, and build a professional network, all of which are valuable for career advancement.

What are the key skills and qualifications needed to thrive as a systems engineering postdoc, and why are they important?

To thrive as a Systems Engineering Postdoc, you generally need a Ph.D. in systems engineering or a related field, along with strong analytical and research skills. Familiarity with modeling and simulation tools (such as MATLAB, Simulink, or Modelica), as well as experience with systems architecture frameworks and relevant programming languages, is often required. Strong problem-solving abilities, collaboration, and effective communication are standout soft skills in this role. These competencies are essential for advancing research, collaborating on interdisciplinary projects, and translating complex system concepts into practical solutions.

What are popular job titles related to Systems Engineering Postdoc jobs?

For Systems Engineering Postdoc jobs, the most frequently searched job titles are:

Infographic showing various Systems Engineering Postdoc job openings in the United States as of August 2026, with employment types broken down into 86% Full Time, 9% Part Time, 4% Contract, and 1% Nights. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $142,070 per year, or $68.3 per hour.

Postdoctoral Research Associate, Systems and Industrial Engineering

Tucson, AZ • On-site

UNIVERSITY OF ARIZONA
Colleges, Universities, and Professional Schools • 10K+ employees
Learn more about UNIVERSITY OF ARIZONAexternal

$60 - $80/hr

Other

Medical, Dental, Vision, Life, PTO

Re-posted 20 days ago

University Of Arizona rating

7.4

Company rating: 7.4 out of 10

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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).
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