University of Tulsa

What you'll study

Programme structure

The PhD combines advanced coursework, a qualifying (or comprehensive) examination, and an original research dissertation. Students take core and elective classes to build deep technical foundations, complete required credit hours of graduate-level study, and engage in sustained research under a faculty advisor. Typical progression includes initial coursework and teaching or research assistantships, followed by candidacy after passing a qualifying exam and then full-time dissertation research leading to a defence.

Typical modules and subjects

• Advanced Algorithms and Complexity
• Machine Learning and Statistical Learning Theory
• Advanced Operating Systems and Distributed Systems
• High-Performance and Parallel Computing
• Computer Vision and Pattern Recognition
• Cybersecurity, Network Security and Applied Cryptography
• Data Mining, Big Data Analytics and Database Systems
• Software Engineering for Research and Large-Scale Systems
• Research Methods and Scientific Communication

Students customise their study plan through electives and directed-reading courses to align with their research focus. Coursework emphasises applied techniques, experimental rigour and reproducible research.

Research areas

Faculty supervision and ongoing projects typically cover applied topics such as machine learning and data analytics, cybersecurity and networked systems, high-performance computing, computer vision, distributed and cloud computing, and software engineering for complex systems. Interdisciplinary work with other departments—such as engineering, natural sciences and business—is encouraged where applications require domain expertise.

Entry requirements

• An accredited master's degree in Computer Science or a closely related discipline is normally required; exceptional candidates with a strong bachelor's degree and significant research experience may be considered.
• A strong academic record demonstrating preparation for advanced study (transcripts required).
• Evidence of research potential: a statement of research interests, a curriculum vitae, and one or more letters of recommendation from faculty or research supervisors.
• A clear research proposal or description of research interests helps match applicants to potential supervisors.
• International applicants must demonstrate English language proficiency through recognised tests unless exempted by university policy.
• Funding considerations: many admitted students receive support through teaching or research assistantships; applicants should review funding and assistantship application processes.

Career prospects

Graduates with a PhD in Applied Computer Science typically move into research-intensive roles. Common career pathways include:

• Academic positions (postdoctoral researcher, lecturer, tenure-track faculty) focused on teaching and research.
• Research and development roles in industry labs and technology companies working on machine learning, big data systems, security or software platforms.
• Senior engineering and technical leadership roles such as principal scientist, research engineer or architect.
• Specialist roles in sectors with strong computing needs, including energy, aerospace, defence, finance and healthcare.
• Founding or contributing to technology startups, or working in applied research at national laboratories and government agencies.

The emphasis on applied research and collaboration at Tulsa positions graduates to translate research into practical systems and commercial solutions.

Why study at University of Tulsa

• Small, research-active department: close mentorship from faculty allows tailored supervision and faster integration into research projects.
• Strong ties with regional industry and applied research partners: opportunities for collaborative projects with companies and organisations in energy, aerospace and related fields.
• Access to computing resources and laboratories that support experimental work in data science, high-performance computing and cybersecurity.
• Funding and professional development: graduate assistantships provide practical teaching and research experience while supporting doctoral study.
• Interdisciplinary collaboration: the university's size and structure make it easier to work across departments on application-driven problems.
• Focus on employability: the programme balances theoretical foundations with applied projects that prepare students for both academic and industry careers.