Course summary

This programme is designed to equip students with a strong foundation in the core principles of NeuroAI, combining theoretical depth with hands-on technical training. Students will develop skills in computational modelling, coding, and algorithm design, while pursuing independent research in a 32-week project. The course also fosters scientific communication skills and provides access to world-class facilities and expert supervision within Cambridge’s vibrant academic community. The educational aims of the course are to: Provide students with relevant experience at first-degree level the opportunity to carry out focused research in this emerging interdisciplinary field under close supervision; Give students the opportunity to acquire or develop technical skills and expertise relevant to their research interests in both neuroscience and AI. The course will also: Provide a strong foundation in the core principles of NeuroAI - exploring topics such as neural networks, connectionist theory, dynamical systems, state-of-the-art AI approaches including transformers and state-space models; Enable hands-on technical training in computational modelling, coding, and algorithm implementation; Allow flexibility for students to explore their specific research interests via a substantial 32-week research project; Train students in academic scientific writing and presentation. As a student in our programme, you will benefit from Cambridge's vibrant academic community in both neuroscience and AI. You will have access to state-of-the-art research facilities including advanced computational resources and high-performance computing clusters. Learning Outcomes By the end of the course, students will be able to demonstrate the following knowledge and understanding: Advanced knowledge of AI, neural computation, and algorithmic approaches at the intersection of neuroscience and AI; Proficiency in implementing computational models and algorithms through hands-on coding experience; In-depth knowledge of the background to their selected research project including research methods and data analysis techniques; A broad understanding of modern research techniques applicable to NeuroAI from the technical lecture series; Knowledge of theoretical approaches relevant to their specialisation and critical thinking in the area; Expertise in research methods, computational modelling, data analysis, and statistics; Originality in applying knowledge with practical understanding of how research and inquiry create and interpret knowledge in this interdisciplinary field. Students will also acquire the following skills and attributes: Ability to analyse critical research literature and contemporary topics in their specialisation areas; Proficiency in explaining complex topics to specialist and non-specialist audiences; Demonstration of technical coding skills and algorithm implementation; Critical thinking and problem-solving approaches to different types of data; Participation in scientific discourse through written materials, code, oral and poster presentations. Continuing If you wish to undertake a PhD following completion of this MPhil, you must be on course to achieve a minimum of a ‘Pass’ and must submit a PhD application in advance of the early December deadline. If shortlisted, you will be invited to a PhD interview in early to mid January. Those who wish to progress to a PhD after completing an MPhil will also be required to satisfy their potential Supervisor, Head of Department and the Faculty Degree Committee that they have the skills and ability to achieve the higher degree.