Colorado State University

What you'll study

This PhD emphasises the design, analysis and evaluation of complex sociotechnical systems in which intelligent algorithms and automation interact with people. Students follow a mix of advanced coursework and sustained independent research. Core topics commonly studied include:

• Foundations of systems engineering and systems thinking, including systems architecture and life‑cycle considerations
• Human factors and human‑systems integration, covering cognitive engineering, ergonomics and decision support
• Artificial intelligence and machine learning methods relevant to intelligent systems, such as perception, planning and adaptive control
• Human‑centred design and usability evaluation, including experimental methods for assessing human performance and trust
• Modelling, simulation and optimisation of socio‑technical systems (agent based, discrete event, continuous models)
• Advanced statistics, stochastic processes and data analysis for inference from human and system data
• Research methods and ethics, preparing students for rigorous experimental and computational research

Programme structure typically combines initial coursework to build breadth, qualifying assessments or a proposal defence to confirm doctoral candidacy, and a programme of supervised original research that culminates in a dissertation. Students often take elective courses across departments such as Computer Science, Psychology, Industrial Engineering and Cognitive Science to suit their research topic. Research frequently involves sensor systems, autonomous platforms, user studies, simulation environments and collaboration with industry or government partners.

Entry requirements

Applicants are normally expected to hold a relevant master’s degree or equivalent in systems engineering, electrical or mechanical engineering, computer science, human factors, cognitive science, or a closely related discipline. A strong academic record and evidence of research potential are essential.

• A master’s degree or equivalent and a transcript demonstrating strong preparation in quantitative and engineering subjects
• A statement of purpose that outlines research interests and proposed fit with faculty expertise
• Academic references that can speak to the applicant's research aptitude
• A current CV or resume, and samples of prior research (publications, technical reports, or a master’s thesis) where available
• Evidence of English language proficiency for non‑native speakers, in line with university requirements

Admission is competitive and dependent on alignment between the applicant’s research interests and faculty supervision capacity. Prospective students are encouraged to contact potential supervisors before applying.

Career prospects

Graduates of the PhD programme progress to research and leadership roles in a wide range of sectors that build and deploy intelligent sociotechnical systems. Typical career paths include:

• Academic positions in systems engineering, human factors, robotics and related fields
• R&D and technical leadership roles in industry sectors such as aerospace, automotive, defence, healthcare technologies and transportation
• Research scientist or engineer positions in national laboratories, government research agencies and independent research institutes
• Senior roles in human–computer interaction, user experience research, and product teams developing AI‑enabled systems
• Consultancy and systems integration roles addressing complex organisational and technical challenges

Graduates are prepared to lead interdisciplinary teams, design safe human‑centred autonomous systems, and contribute to policy and standards where human‑systems integration is critical.

Why study at Colorado State University

Colorado State University offers an interdisciplinary environment that supports systems‑level research combining engineering, human factors and intelligent systems. The university provides access to faculty with expertise in human‑systems integration, autonomy, machine learning and experimental methods, and encourages collaborations across departments to address real‑world problems.

• Strong emphasis on applied and fundamental research with opportunities to work on experimental testbeds, simulation platforms and field deployments
• Opportunities for collaboration with industry partners, regional technology firms and national research organisations
• Supportive graduate training including seminars, teaching opportunities and professional development for academic and non‑academic careers
• A campus culture that fosters interdisciplinary mentorship and close faculty‑student research relationships

These features make Colorado State University a suitable place to develop the advanced technical, experimental and leadership skills needed to design and evaluate intelligent systems that interact effectively and safely with people.