University of San Diego

What you'll study

The PhD emphasises rigorous foundations in systems engineering, human-systems interaction and intelligent systems, blended with substantial original research. Early coursework typically covers systems modelling and simulation, optimisation methods, control theory, human factors and cognitive systems engineering, machine learning for decision support, experimental design and advanced statistics.

After completing core and elective coursework, students progress to qualifying assessments and a research plan. Typical advanced topics and module themes include:

• Human Systems Engineering: human-centred design, workload and performance modelling, usability evaluation and human-machine teaming.
• Intelligent Systems: supervised and unsupervised learning, reinforcement learning, reasoning under uncertainty and autonomous decision-making.
• Systems Methods: systems architecture, model-based systems engineering, optimisation and large-scale simulation.
• Experimentation and Measurement: experimental methods, psychometrics, sensor fusion and data-driven validation.
• Ethics and Social Impact: responsible AI, safety, privacy and socio-technical risk assessment.

The research component culminates in a dissertation that contributes new theory, methods or applications in intelligent human-systems. Students typically work closely with faculty advisors from the Shiley-Marcos School of Engineering and often collaborate across departments such as electrical engineering, computer science, cognitive science and business.

Entry requirements

Applicants are expected to hold a relevant master's degree in systems engineering, engineering, computer science, human factors, cognitive science or a closely related discipline. A strong academic record, evidence of research potential (for example a thesis, publications or technical reports), and relevant technical skills in programming and quantitative methods are highly desirable.

Admissions materials normally include a statement of purpose that outlines research interests, a CV, academic transcripts, and letters of recommendation from academic or professional referees familiar with the applicant's research ability. Some applicants with exceptional professional experience and demonstrated research aptitude may be considered without a master's degree. Standardised tests may be considered according to current school policy; consult the programme for the latest guidance.

Career prospects

Graduates obtain roles in academic research and teaching, or in industry and government organisations that require expertise at the intersection of humans and intelligent systems. Common career paths include:

• University faculty and postdoctoral research positions in systems engineering, human factors or AI.
• R&D roles in aerospace, defence and autonomous systems, working on human-autonomy teaming and system safety.
• Industry research scientist or senior engineer positions in robotics, autonomous vehicles, healthcare technologies and human-centred AI.
• Systems architect or lead engineer roles in complex systems integration and model-based systems engineering.
• Product and UX research leadership in technology companies focusing on intelligent interfaces and decision-support tools.

Why study at University of San Diego

The University of San Diego offers this PhD within the Shiley-Marcos School of Engineering, providing a small-cohort, mentorship-driven environment where students work directly with faculty on applied and theoretical problems. The university's location in the San Diego region gives strong proximity to a dense ecosystem of defence contractors, robotics firms, biotech companies and startups, supporting collaboration and internship opportunities.

Research-active faculty bring expertise across human factors, machine learning and systems engineering, and students benefit from available laboratory facilities and interdisciplinary partnerships across neighbouring departments. The programme emphasises responsible innovation, preparing graduates to design intelligent systems that are safe, usable and societally beneficial.