Massachusetts Institute of Technology

What you'll study

This programme is centred on integrating experimental psychology, cognitive neuroscience and computational modelling. You will take advanced courses in topics such as cognitive neuroscience, perception and attention, language and cognition, learning and memory, and computational cognitive modelling. Methodological modules typically cover neuroimaging and electrophysiological techniques, statistical methods for behavioural and neural data, machine learning for cognitive modelling, and programming for data analysis (Python/MATLAB).

The course structure emphasises independent research: students usually complete a combination of coursework and supervised laboratory research, participate in lab meetings and seminars, and produce a substantive thesis based on original empirical or computational work. Many students undertake laboratory rotations early in their programme to find the best match for their research interests.

• Core topics: cognitive neuroscience, computational models of cognition, experimental design and statistics for behavioural/neural data.
• Typical electives: language processing, visual perception, decision making, reinforcement learning, cognitive development, social cognition.
• Research training: neuroimaging (fMRI), electrophysiology (EEG/MEG), single-unit recording (where relevant), computational simulation, data science pipelines.
• Capstone: an original research thesis, often leading to publications or conference presentations.

Entry requirements

Applicants are expected to hold a good honours degree (or equivalent) in a relevant discipline such as psychology, neuroscience, cognitive science, computer science, engineering, mathematics or a related quantitative field. Strong quantitative skills and programming experience are highly desirable because of the programme’s computational and statistical components.

• Academic transcripts demonstrating strong performance in relevant coursework.
• A statement of purpose outlining research interests and fit with specific labs or faculty.
• Two or three academic references, preferably including at least one who can attest to research potential.
• A CV or résumé showing research experience, technical skills and relevant coursework.
• Proof of English language proficiency for applicants whose first language is not English (where required).

Research experience (e.g. undergraduate projects, internships or publications) is a major advantage. Applicants should review faculty research interests and identify potential supervisors; admission is competitive and often depends on the availability of faculty mentors and laboratory placements.

Career prospects

Graduates leave the programme prepared for research and technical roles across academia, industry and clinical research settings. Common career paths include further PhD study in cognitive science, neuroscience, psychology or related fields; research scientist or engineer roles in artificial intelligence and machine learning groups; data scientist or quantitative analyst positions; and user experience or human–computer interaction researcher roles.

Other opportunities include roles in neurotechnology companies, clinical research coordination, science policy and communication, and entrepreneurship. The programme’s combination of experimental, computational and analytical training is valued by employers who need expertise in designing experiments, analysing complex datasets and building computational models of behaviour.

Why study at Massachusetts Institute of Technology

MIT offers an intensely interdisciplinary environment that brings together faculty and students from brain and cognitive sciences, computer science, engineering and related departments. Students benefit from access to world-class research centres and facilities, including cognitive neuroscience and neuroimaging laboratories, computational resources and collaborative institutes that span fundamental and applied research.

The institute’s strong culture of collaboration and innovation also provides opportunities to work on translational projects, engage with industry partners, and take advantage of entrepreneurship and technology-transfer resources. Close interactions with faculty who are leaders in cognitive and computational neuroscience, together with a broad seminar programme and a high density of active research groups, make MIT a distinctive environment for developing advanced research skills in cognitive science.