These courses, which are commonly referred to as Part III, are both nine-month taught Masters courses in mathematics. Part III provides excellent preparation for mathematical research and is also a valuable course in mathematics and its applications for students who want further training before taking posts in industry, research or teaching establishments.

Each year the faculty offers up to 80 lecture courses in Part III, covering an extensive range of pure mathematics, probability, statistics, applied mathematics and theoretical physics. These are designed to cover advanced parts of the subjects that are not normally covered in a bachelor’s course, but which are an indispensable preliminary to independent study and research. Students can choose various combinations of courses, although naturally they tend to select groups of cognate courses. Example classes and associated marking of example sheets are provided as complementary support to lectures.

The MPhil is offered by the Faculty of Mathematics as a full-time period of research and introduces students to research skills and specialist knowledge. Its main aims are: (i) to give students with relevant experience at first-degree level the opportunity to carry out focused research in the discipline under supervision; and (ii) to give students the opportunity to acquire or develop skills and expertise relevant to their research interests.

This is a 12-month full-time programme and involves minimal formal teaching: students are integrated into the research culture of the Department of Pure Mathematics & Mathematical Statistics (DPMMS), or the Department of Applied Mathematics and Theoretical Physics (DAMTP), as appropriate. They may attend the Departments’ programmes of research seminars and other postgraduate courses, but most research training is overseen by their research supervisor, and, where appropriate, within a research group. Opportunities to develop research and transferable skills also exist through attendance at training sessions organised at Department, School or University level as part of the wider postgraduate programme, and informally through mentoring by fellow students and members of staff.

The aim of this 9-month course is to develop research skills and methodology in computer science. Students select five taught modules from a wide range of advanced topics in computer science from networking and systems measurements to category theory, and topics in natural language processing, alongside an independent research project. The taught modules are delivered in a range of styles: traditional lecture courses, lecture courses with associated practical classes, reading clubs, and seminar style modules. Additionally, students take an ungraded course in research skills which includes core and optional topics. 

This 11-month course has a unique joint emphasis on both machine learning and machine intelligence. The course is split into four specialised pathways, which set the scope for the dissertation, and which each have different module combinations. The four pathways are: (i) Machine Learning; (ii) Speech and Language Processing; (iii) Computer Vision and Robotics; (iv) Human-Computer Interaction. The course aims to teach the state of the art in machine learning and machine intelligence; to give students the skills and expertise necessary to take leading roles in industry; and to equip students with the research skills necessary for doctoral study.

This 12-month course aims to introduce students to research skills and specialist knowledge. The course provides world-class education on high performance computing and advanced algorithms for numerical simulation at continuum and atomic-scale levels. Its main aims are to:

• encourage and pursue research of the highest quality in Scientific Computing and its applications.
• provide education in Scientific Computing of the highest quality at a postgraduate level and to produce graduates of the calibre sought by industry, the professions, and the public service.
• provide training for the academic researchers and teachers of the future.

This 10-month course prepares science graduates for data intensive research careers by providing advanced training in Statistical Analysis, Machine Learning, and Research Computing, and their application to current research frontiers. The course aims to:

• train exceptional research scientists to design and implement data analysis pipelines for increasingly complex data sets;
• enable development of data science skills which can be applied in science, health and economic areas;
• train students in data science techniques and algorithm building for modern computer architectures and software development;
• increase availability of open science, specifically reproducibility of results and the creation of public data analytic codes.

This is a three to four-year research programme culminating in submission and examination of a thesis containing substantial original work. PhD students carry out their research under the guidance of a supervisor, and research projects are available from a wide range of subjects studied within the Department.

Students admitted for a PhD will normally have completed preparatory study at a level comparable to the Cambridge Part III (MMath/MASt) course. A significant number of these PhD students secure post-doctoral positions at institutions around the world and become leading researchers in their fields.

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This course is a three to four year programme culminating in the submission and examination of a single research thesis.  Students joining the course will often have completed prior study at a level comparable to the Part III (MMath/MASt) course and many have postgraduate experience. Students, therefore, begin their PhD research with a good understanding of advanced material, which they build on in various ways throughout the course of their PhD studies.

Research in DPMMS can be divided into the following broad areas: Algebra, Algebraic Geometry, Analysis and Partial Differential Equations, Combinatorics, Differential Geometry and Topology, Number Theory, Information and Finance, Probability, and Statistics. The boundaries between such areas are not rigid, however, and staff may contribute to more than one area.

Find out more here.