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Graduate Admissions

About the Department of Pure Mathematics and Mathematical Statistics

The Department of Pure Mathematics and Mathematical Statistics (DPMMS) is one of two Mathematics Departments at the University of Cambridge, the other being the Department of Applied Mathematics and Theoretical Physics (DAMTP). The two departments together constitute the Faculty of Mathematics, and are responsible for the teaching of Mathematics and its applications within the Mathematical Tripos. The Statistical Laboratory is a sub-department of DPMMS.

4 courses offered in the Department of Pure Mathematics and Mathematical Statistics

This course, commonly referred to as Part III, is a nine-month taught master's course in mathematics. It is excellent preparation for mathematical research and it is also a valuable course in mathematics and in its applications for those who want further training before taking posts in industry, teaching, or research establishments.

Students admitted from outside Cambridge to Part III study towards the Master of Advanced Study (MASt). Students continuing from the Cambridge Tripos for a fourth-year study towards the Master of Mathematics (MMath). The requirements and course structure for Part III are the same for all students irrespective of whether they are studying for the MASt or MMath degree.

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Cambridge Mathematics of Information (CMI) offers a four-year PhD programme, with a structured first year. Research areas in CMI range widely across the field of `data science’ including statistics and probability; applied, pure and computational analysis; and the theory and modelling of complex, dynamical and physical systems. Training, especially in the first year, emphasises not only individual study but also teamwork, communication and engagement with users of mathematics. Students are based at the Centre for Mathematical Sciences, which houses the Department of Pure Mathematics and Mathematical Statistics, Department of Applied Mathematics and Theoretical Physics, Statistical Laboratory, Isaac Newton Institute and Betty and Gordon Moore Library.

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This course, commonly referred to as Part III, is a nine-month taught master's course in mathematics. It is excellent preparation for mathematical research and it is also a valuable course in mathematics and in its applications for those who want further training before taking posts in industry, teaching, or research establishments.

Students admitted from outside Cambridge to Part III study towards the Master of Advanced Study (MASt). Students continuing from the Cambridge Tripos for a fourth-year study towards the Master of Mathematics (MMath). The requirements and course structure for Part III are the same for all students irrespective of whether they are studying for the MASt or MMath degree.

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This course is a three-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 our Part III (MMath/MASt) course and many have postgraduate experience. Our 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. Our PhD students might have written several papers before they submit their dissertation, and can go on to win academic positions at leading institutions around the world.

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4 courses also advertised in the Department of Pure Mathematics and Mathematical Statistics

From the Department of Earth Sciences

The UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) trains researchers (through several multidisciplinary cohorts) to be uniquely equipped to develop and apply leading-edge computational approaches to address critical global environmental challenges by exploiting vast, diverse and often currently untapped environmental data sets. Embedded in the outstanding research environments of the University of Cambridge and the British Antarctic Survey (BAS), the AI4ER CDT addresses problems that are relevant to building resilience to environmental hazards and managing environmental change. The primary application areas are:

  • Weather, Climate and Air Quality
  • Natural Hazards
  • Natural Resources (food, water & resource security and biodiversity)

Students in the CDT cohorts engage in a one-year MRes degree in Physical Sciences (Environmental Data Science) which includes a taught component and a major research element, followed by a three-year PhD research project. Students will receive high-quality training in research, professional, technical and transferable skills through a focused core programme with an emphasis on the development of data science skills through hackathons and team challenges. Training is guided by personalised advice and the expertise of a network of partners in industry, government, the third sector and beyond.

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From the School of the Biological Sciences

The Cambridge Bioscience DTP is a four year PhD programme that aims to create highly skilled and employable people. The programme offers training across 21 University Departments and 5 Partner Institutes providing access to a wide range of research areas related to the strategic themes of the BBSRC.

During the programme, students will undertake two ten-week rotations in different labs before commencing their PhD. They will receive training in a variety of areas including but not limited to statistics, programming, ethics, data analysis, scientific writing and public engagement. Students will also undertake a 12-week internship (PIPS). Students will be expected to submit their thesis at the end of the fourth year and no further write-up period will be allowed.

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From the Department of Physics

The development of new materials lies at the heart of many of the technological challenges we currently face, for example creating advanced materials for energy generation. Computational modelling plays an increasingly important role in the understanding, development and optimisation of new materials.

This four-year doctoral training programme on computational methods for material modelling aims to train scientists not only in the use of existing modelling methods but also in the underlying computational and mathematical techniques. This will allow students to develop and enhance existing methods, for instance by introducing new capabilities and functionalities, and also to create innovative new software tools for materials modelling in industrial and academic research.

The first year of the doctoral training programme is provided by the existing MPhil course in Scientific Computing, which has a research and a taught element, as well as additional training elements. The final three years consist of a PhD research project, with a student-led choice of projects from a large pool contributed by researchers associated with the CDT.

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From the Department of Physics

The MPhil programme in Scientific Computing is based in the Department of Physics and is a full-time 12-month course which aims to provide education of the highest quality at master’s level. Covering topics of high-performance scientific computing and advanced numerical methods and techniques, it produces graduates with rigorous research and analytical skills, who are well equipped to proceed to doctoral research or directly into employment in industry, the professions, and public service. It also provides training for the academic researchers and teachers of the future, encouraging the pursuit of research in computational methods for science and technology disciplines, thus being an important gateway for entering PhD programmes containing a substantial component of computational modelling.

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Department Members


Professor J. Norris
Head of Department

  • 42 Academic Staff
  • 50 Postdoctoral Researchers
  • 80 Graduate Students
  • 1000 Undergraduates

https://www.dpmms.cam.ac.uk/

Research Areas