Pace University

What you'll study

The programme combines advanced coursework, rigorous research training and a sustained dissertation project. Early study typically covers core foundations — advanced algorithms, theory of computation, and formal methods — alongside applied topics such as machine learning, data analytics, distributed and cloud systems, software engineering, and cybersecurity.

Students participate in research seminars and specialised electives that reflect faculty expertise and emerging industry needs (for example deep learning, natural language processing, computer vision, privacy-preserving computation, and IoT systems). The curriculum emphasises:

• Research methods and practicum: training in experimental design, statistical methods, reproducibility, and scientific communication.
• Seminars and teaching experience: opportunities to present work, lead reading groups and gain teaching skills through assistantships.
• Qualifying and comprehensive assessments: milestone examinations to demonstrate mastery of core material and readiness to embark on dissertation research.
• Doctoral dissertation: an original research project supervised by faculty that leads to a written thesis and oral defence.

Entry requirements

Applicants are expected to hold a strong undergraduate degree in computer science or a closely related field; many successful candidates also hold a relevant master’s degree. Typical requirements include:

• A bachelor's degree in computer science, engineering, mathematics or a related discipline; a relevant master’s degree is advantageous.
• Academic transcripts demonstrating strong performance in core technical coursework (algorithms, data structures, mathematics, programming).
• A statement of purpose describing research interests and professional goals, and how they align with the department's expertise.
• Letters of recommendation (usually two or three) from academic or professional referees familiar with the applicant’s research potential.
• A curriculum vitae outlining relevant research, publications, industry experience or projects.
• Proof of English language proficiency for international applicants, where required.

Standardised tests such as the GRE may be considered where submitted, but applicants should consult the programme for current testing policies. Strong research experience, demonstrated potential for independent work, and a clear match with faculty research areas significantly strengthen an application.

Career prospects

Graduates of an applied computer science PhD enter a range of careers that require deep technical expertise and research leadership. Typical paths include:

• Academic positions as tenure-track faculty, postdoctoral researchers and lecturers.
• Industrial research and development roles — research scientist, senior data scientist, machine learning engineer — in technology companies, finance, healthcare and telecommunications.
• Technical leadership roles such as chief technology officer, head of AI, or principal engineer in startups and established firms.
• Specialist roles in government labs and research institutes focusing on cybersecurity, AI ethics, or large-scale systems.

The programme's combination of theory and applied research supports both publication-oriented academic careers and roles that translate research into products and services.

Why study at Pace University

Pace University’s strengths include an applied, interdisciplinary approach to computer science and close ties to industry in the New York metropolitan area. Students benefit from:

• Faculty mentorship: supervision by faculty active in applied research areas who collaborate with industry and other academic institutions.
• Location and industry connections: proximity to a large technology and finance ecosystem that offers internships, collaborative projects and employment opportunities.
• Research facilities and centres: access to labs and centres focused on data science, cybersecurity and software systems, enabling experiential research and partnerships.
• Small cohorts and personalised training: a doctoral environment that emphasises close faculty-student interaction and tailored development plans.
• Interdisciplinary opportunities: collaboration across business, healthcare, and design faculties to apply computing research to real-world problems.

Prospective students should contact the department to discuss fit with faculty research and to learn about available funding, assistantships and research projects.