NeuroAI and Intelligent Systems
Basic Information
- Course ID
852a6d93-9abb-44c0-9fa1-b8050ca15411- Option ID
1827b4d0-2066-4b38-a79e-83ea95422613- Provider
- University of Cambridge
- Type
- postgraduate
- Academic Year
- 2026
- Source
- ucas
- Created
- 2/25/2026, 11:18:43 PM
- Updated
- 3/28/2026, 10:38:37 AM
Other Options for this Course
No other options
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"assessmentMethods": "Thesis / Dissertation\nThe assessment structure for the course is divided into three parts:\n\nSection 2: Research project literature review (30% of your mark)\n\nYou will undertake a comprehensive literature review related to your research project. This review, with a maximum word limit of 5,000 words, will serve as a foundation for your research project, providing essential background information and contextualising the significance of your chosen area of study. Your literature review will be assessed based on the depth of your research, critical analysis, and ability to synthesise and present information effectively.\n\nSection 3: Research project methods and outcomes (40% of your mark)\n\nYou will focus on presenting the outcomes of your research project. This component will require you to articulate the aims, methods, results, data analysis, and discussion of your project within a maximum word limit of 5,000 words. You will showcase your research skills, analytical thinking and ability to draw meaningful conclusions from your data.\n\nEssays\nSection 1: Extended essay and codebook (30% of your mark)\n\nYou will produce an extended essay (up to 5,000 words) introducing and discussing a particular approach within NeuroAI. This will be accompanied by a codebook demonstrating an implementation of the technical approach applied to a dataset or simulated task-design. The assessment includes an in-person viva where you'll discuss your work and demonstrate your understanding of the code. Two markers will assess these components to ensure parity of standards.\n\nOther\nThese assessments allow you to demonstrate your academic abilities and provide an opportunity for you to contribute to the emerging field of NeuroAI. All sections will be evaluated by experienced faculty members who will assess your work based on its quality, originality and scientific rigor.\n\nBy employing this multimodal assessment approach, we aim to evaluate not only your knowledge and understanding but also your critical thinking abilities, research capabilities, and technical skills. This assessment framework ensures that your progress and development as a NeuroAI researcher are effectively recognised. We are committed to providing you with constructive feedback and support throughout your journey, enabling you to grow and excel in this exciting interdisciplinary field.",
"entryRequirements": "Applicants for this course should have achieved a UK Good II.i Honours Degree.\n\nIf your degree is not from the UK, please check International Qualifications to find the equivalent in your country.\n\nWe believe that diversity of thought and perspective is essential for advancing scientific knowledge. Therefore, we welcome students from a wide range of academic backgrounds. This might include: neuroscience, medicine, psychology, biology, computer science, engineering, or other, related disciplines. Our program thrives on interdisciplinary collaboration, and we encourage you to bring your unique insights and experiences to contribute to the rich tapestry of ideas.",
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"suggest": "NeuroAI and Intelligent Systems. University of Cambridge. Artificial intelligence",
"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.\n\nThe educational aims of the course are to:\n\nProvide students with relevant experience at first-degree level the opportunity to carry out focused research in this emerging interdisciplinary field under close supervision;\nGive students the opportunity to acquire or develop technical skills and expertise relevant to their research interests in both neuroscience and AI.\nThe course will also:\n\nProvide 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;\nEnable hands-on technical training in computational modelling, coding, and algorithm implementation;\nAllow flexibility for students to explore their specific research interests via a substantial 32-week research project;\nTrain students in academic scientific writing and presentation.\nAs 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.\n\nLearning Outcomes\nBy the end of the course, students will be able to demonstrate the following knowledge and understanding:\n\nAdvanced knowledge of AI, neural computation, and algorithmic approaches at the intersection of neuroscience and AI;\nProficiency in implementing computational models and algorithms through hands-on coding experience;\nIn-depth knowledge of the background to their selected research project including research methods and data analysis techniques;\nA broad understanding of modern research techniques applicable to NeuroAI from the technical lecture series;\nKnowledge of theoretical approaches relevant to their specialisation and critical thinking in the area;\nExpertise in research methods, computational modelling, data analysis, and statistics;\nOriginality in applying knowledge with practical understanding of how research and inquiry create and interpret knowledge in this interdisciplinary field.\nStudents will also acquire the following skills and attributes:\n\nAbility to analyse critical research literature and contemporary topics in their specialisation areas;\nProficiency in explaining complex topics to specialist and non-specialist audiences;\nDemonstration of technical coding skills and algorithm implementation;\nCritical thinking and problem-solving approaches to different types of data;\nParticipation in scientific discourse through written materials, code, oral and poster presentations.\nContinuing\nIf 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.",
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