University of Colorado Boulder

What you'll study

The programme centres on original research in mechatronics, robotics and automation supported by advanced coursework. Typical study areas include advanced dynamics and kinematics, nonlinear and optimal control, motion planning, perception and computer vision for robotics, machine learning for autonomous systems, embedded and real-time systems, sensors and actuators, human–robot interaction and industrial automation.

Structure normally combines a tailored set of graduate-level courses with a qualifying examination or assessment, a research proposal, supervised laboratory research and a doctoral dissertation. Students frequently take classes across departments — for example mechanical engineering, electrical engineering and computer science — to build interdisciplinary competence in systems integration, software, hardware and experimental methods.

Practical training is emphasised through access to departmental and campus facilities for fabrication, rapid prototyping, electronics labs, motion-capture spaces and mobile-robot testing areas. Doctoral researchers commonly collaborate with campus research centres and institutes to apply their work to areas such as field robotics, manufacturing automation, autonomous vehicles and biomedical devices.

Entry requirements

• Academic background: A relevant master’s degree is typical (engineering, computer science, robotics or closely related discipline); exceptionally qualified applicants with a strong bachelor’s degree and research experience may be considered.
• Technical preparation: Solid foundations in mathematics (linear algebra, calculus, differential equations), dynamics, control theory, programming and electronics or embedded systems are expected. Prior coursework or experience in robotics, machine learning or signal processing strengthens applications.
• Application materials: A curriculum vitae, academic transcripts, a statement of purpose describing research interests and fit with faculty, and three letters of recommendation are normally required. International applicants must demonstrate English proficiency in accordance with university policy.
• Research fit: Admission emphasises match with faculty research areas — applicants are encouraged to identify potential advisers and relevant research groups in their statement of purpose.
• Funding: Many doctoral students are supported by research assistantships, teaching assistantships or fellowships; prospective students should contact potential advisers about available positions.

Career prospects

Graduates go on to careers in academic research and teaching, industrial R&D and technical leadership. Typical roles include robotics research scientist, controls engineer, autonomy software engineer, systems integration lead, chief engineer for automation projects, and technical founder in robotics startups. Employers span autonomous-vehicle companies, aerospace and defence contractors, advanced manufacturing firms, medical device companies, national research laboratories and university research groups.

Doctoral training also prepares graduates for interdisciplinary roles that combine hardware, software and data-driven methods — for example leading teams that deploy robotic systems in unstructured environments, developing perception and planning stacks, or translating prototype systems into scalable industrial solutions.

Why study at University of Colorado Boulder

CU Boulder offers a strong interdisciplinary environment for robotics and automation research, with faculty across mechanical engineering, electrical engineering, computer science and the ATLAS Institute working on complementary topics. The campus provides substantial hands-on resources — machine shops, electronics labs, fabrication facilities and dedicated robotics testbeds — that support experimental doctoral work.

Students benefit from collaborative research centres, opportunities to work with regional technology firms and startup communities, and connections to industry projects that accelerate technology transfer. The university’s emphasis on cross-department collaboration, entrepreneurial support and plentiful research funding opportunities makes it a productive setting for doctoral candidates aiming to advance both fundamental knowledge and applied automation technologies.