Brandeis University

What you'll study

The PhD in Applied Computer Science combines advanced coursework with sustained original research. Early in the programme students take core and elective graduate courses to build a strong foundation in theoretical and practical aspects of computing. Typical subject areas include:

• Machine Learning and Artificial Intelligence — probabilistic models, deep learning, reinforcement learning and statistical learning theory.
• Data Science and Big Data — data mining, scalable analytics, database systems and data visualisation.
• Systems and Networks — distributed systems, operating systems, cloud computing and networking.
• Algorithms and Theory — advanced algorithms, complexity theory and optimisation methods.
• Security and Privacy — cryptography fundamentals, privacy-preserving computation and system security.
• Computational Biology and Bioinformatics — computational genomics, biological data analysis and interdisciplinary projects with life-science groups.
• Human–Computer Interaction and Robotics — user-centred design, interaction technologies and applied robotics for some research tracks.

Programme structure generally includes a period of coursework, qualifying/comprehensive examinations or milestones, the development of a dissertation proposal, and completion of an original doctoral dissertation. Students typically participate in research groups, attend and present at seminars and conferences, and often take part in teaching undergraduate courses or serving as teaching assistants.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in computer science, engineering, mathematics or a closely related field; many successful applicants also hold a master’s degree. Typical preparation includes strong programming skills, mathematical background (discrete mathematics, linear algebra, probability and statistics), and prior coursework in algorithms and systems or machine learning.

Application materials usually include:

• Academic transcripts from all post-secondary institutions attended.
• A research statement describing past work, research interests and potential faculty mentors.
• Letters of recommendation from academic or professional referees who can speak to research potential.
• A CV outlining relevant projects, publications, internships and technical skills.
• Proof of English proficiency for applicants whose first language is not English, where required.

Standardised test requirements (such as the GRE) vary over time; applicants should consult the department for current policy. Successful applicants typically demonstrate evidence of research aptitude—such as independent projects, publications, or research assistant experience—and a clear fit with faculty research areas.

Career prospects

Graduates of the PhD programme pursue a range of careers across academia, industry and government. Common paths include:

• Academic and research careers: postdoctoral positions and faculty roles in universities, focusing on continued research and teaching.
• Industry research: research scientist or applied researcher roles in technology companies, industrial research labs and startups working on AI, systems and data science.
• Technical leadership: senior engineer, principal scientist, or engineering manager positions leading applied research and development teams.
• Interdisciplinary roles: computational biologists, bioinformaticians or data scientists in healthcare, biotech and pharmaceutical companies, leveraging collaborations between computer science and life sciences.
• Public sector and non-profit research: positions in national labs, think tanks and organisations applying computing to social-good, security and public policy challenges.

The programme’s emphasis on applied research, coupled with opportunities to collaborate with nearby research institutions and industry in the Boston–Cambridge technology ecosystem, supports strong placement into research and technical roles.

Why study at Brandeis University

Brandeis offers a PhD environment characterised by small cohorts and close faculty mentoring, enabling intensive supervisory relationships and personalised research development. The Computer Science Department has active research groups across machine learning, systems, security and computational biology, encouraging interdisciplinary projects with neighbouring departments such as biology, neuroscience and economics.

Students benefit from the university’s research culture and access to collaborative centres and resources, as well as geographic proximity to Boston and Cambridge—regions with dense networks of universities, hospitals and tech companies that provide seminar series, internships and employment opportunities. The programme emphasises both rigorous theoretical foundations and real-world applied work, preparing graduates for diverse careers in research, industry and beyond.