Rochester Institute of Technology

What you'll study

The programme combines advanced coursework with sustained original research leading to a doctoral dissertation. Core elements include advanced topics in algorithms, systems, machine learning and data science; research methods and experimental design; and seminars that expose students to current literature across computing and information sciences.

• Core and elective coursework: graduate-level courses in areas such as artificial intelligence and machine learning, cybersecurity and privacy, software engineering, data analytics and databases, computer networks, human–computer interaction, and computational modelling.
• Research rotation and lab placement: early-stage research experience with faculty mentors in specialised labs and centres to identify dissertation topics and develop proposals.
• Qualifying and comprehensive examinations: assessments that test breadth and depth of knowledge and readiness to undertake independent research.
• Dissertation research: sustained independent research under the supervision of a faculty advisor, culminating in a written dissertation and oral defence.
• Seminars and teaching experience: participation in departmental seminars, opportunities to publish in conferences and journals, and supervised teaching or mentoring responsibilities as part of professional development.

Entry requirements

Applicants are normally expected to hold a relevant master's degree (or equivalent) in computer science, information sciences, engineering, mathematics or a closely related discipline, with a strong academic record. Exceptional candidates with a strong bachelor’s degree and research experience may also be considered.

• Academic preparation: evidence of advanced coursework in core computing subjects such as algorithms, systems, programming, and mathematics.
• Research experience: prior research, publications, technical reports or significant project work that demonstrates potential for independent research.
• Application materials: statement of purpose outlining research interests, curriculum vitae, academic transcripts, and letters of recommendation from academic or professional referees.
• Faculty fit: alignment of your research interests with faculty expertise; applicants are encouraged to identify potential advisors in their statement of purpose.
• English language proficiency: for applicants whose first language is not English, acceptable scores on recognised English tests or equivalent evidence of proficiency.

Career prospects

Graduates of the PhD programme pursue a range of careers that leverage advanced research skills and technical leadership. Common paths include tenure-track academic positions, postdoctoral research appointments, and research scientist roles in corporate and government laboratories.

• Industry research & development: positions in R&D teams at technology companies, start-ups and research centres working on machine learning, analytics, cybersecurity, software systems and related fields.
• Academia and teaching: faculty roles in universities and colleges, along with opportunities to lead research groups and secure competitive grants.
• Technical leadership: senior engineering and architecture positions that require expertise in designing and evaluating complex information systems.
• Government and non-profit research: roles in national labs, standards organisations and research-focused NGOs addressing public-interest computing problems.

Why study at Rochester Institute of Technology

RIT offers a research-intensive environment with strengths in applied computing and interdisciplinary collaboration. The university’s computing and information sciences faculty have expertise across a broad range of areas, enabling students to work on problems that span theory, systems and applications.

• Research infrastructure: access to dedicated labs, high-performance computing resources and specialised research centres that support experimental and applied work.
• Industry connections and experiential learning: strong links with regional and national technology companies provide collaboration opportunities, internships and pathways to applied research projects.
• Support for graduate students: opportunities for funded assistantships, mentorship from active researchers, and professional development in teaching, grant writing and communication.
• Interdisciplinary opportunities: the university encourages cross-college collaborations with fields such as engineering, imaging science, data science and business to address real-world challenges.