University of Virginia

What you'll study

The programme centres on independent research leading to a doctoral dissertation, supported by advanced coursework and departmental seminars. Core areas of study include algorithms and theory, machine learning and artificial intelligence, systems and networking, security and privacy, human–computer interaction, databases and information retrieval, and data science. Students typically complete a set of required graduate courses to build a rigorous theoretical and practical foundation, elective seminars to deepen domain knowledge, and participatory research seminars to present and critique ongoing work.

Typical components of the PhD include:

• Coursework: Advanced graduate courses in algorithms, machine learning, operating systems, programming languages, security, and databases.
• Research rotations or practicum: Short-term research projects early in the programme to help students identify a research advisor and topic.
• Qualifying examinations: Departmental milestone assessments that ensure readiness to undertake independent research.
• Teaching or assistantship experience: Opportunities to teach or assist undergraduate/graduate courses as part of professional development.
• Dissertation and defence: Original research culminating in a written dissertation and public defence.

Entry requirements

Applicants are expected to hold a strong undergraduate degree in computer science, information science, engineering, mathematics or a closely related field; many successful applicants also hold a relevant master’s degree. A strong academic record in core subjects (algorithms, data structures, discrete mathematics, probability, and systems) and demonstrated research potential are essential.

Typical application materials include:

• Academic transcripts from all post-secondary institutions attended.
• Letters of recommendation (usually three) that can attest to academic ability and research potential.
• Statement of purpose outlining research interests and fit with faculty expertise.
• Research experience, such as publications, technical reports, or project portfolios, is highly valued.
• Evidence of programming and mathematical skills, demonstrated through coursework, projects, or work experience.

International applicants should demonstrate sufficient English proficiency in line with university requirements. The department evaluates applications holistically; specific standardised test requirements (if any) and funding offers vary, so prospective applicants should consult the department’s admissions pages for current policies.

Career prospects

Graduates of the PhD in Computer and Information Sciences pursue a range of careers in academia, industry and government. Common paths include:

• Academic careers: tenure-track faculty positions and postdoctoral research appointments at universities worldwide.
• Industrial research & development: research scientist or senior engineer roles in technology companies, national and international labs, and corporate research centres.
• Data science and AI leadership: senior data scientist, machine learning engineer, or technical lead positions applying research to large-scale products and services.
• Startups and entrepreneurship: founding or joining technology startups where deep technical expertise drives product innovation.
• Government and public sector research: roles in national labs, defence research, or policy organisations that require advanced technical analysis.

The programme emphasises communication, teaching and project leadership skills to prepare graduates to lead research teams, secure competitive funding, and translate research into real-world impact.

Why study at University of Virginia

The University of Virginia combines a strong, research-active Department of Computer Science with interdisciplinary collaboration across the School of Engineering and Applied Science, the School of Data Science and other University units. Students benefit from close faculty mentorship, access to high-performance computing resources, and opportunities to collaborate with research centres and industry partners.

Faculty research strengths span systems, security, machine learning, and information management, providing a broad choice of advisors and research topics. The department supports a collaborative culture with regular seminars, reading groups and workshops that connect students with peers and visiting researchers. Additionally, Charlottesville’s regional tech ecosystem and the University’s alumni network provide routes to internships, partnerships and careers after graduation.

Financial support is typically available to doctoral students in the form of research assistantships, teaching assistantships and fellowships; prospective students should review department information on funding and reach out to potential advisors to discuss research alignment and support.