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Postgraduate Admissions

About the Department of Applied Mathematics and Theoretical Physics

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

5 courses offered in the Department of Applied Mathematics and Theoretical Physics

This course, commonly referred to as Part III, is a nine-month taught masters course in mathematics. It is excellent preparation for mathematical research and it is also a valuable course in mathematics and 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, or whether they applied through the Applied Mathematics (MASA), Pure Mathematics (MASP), Mathematical Statistics (MASS), or Theoretical Physics (MASTH) application stream.

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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 our PhD students secure post-doctoral positions at institutions around the world and become leading researchers in their fields.

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Awaiting Approval

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The MPhil in Computational Biology course is aimed at introducing students in the biological, mathematical and physical sciences to quantitative aspects of modern biology and medicine, including bioinformatics.  The course was developed by the Cambridge Computational Biology Institute (now C2D3 Computational Biology) and is run by the Department of Applied Mathematics and Theoretical Physics.

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This course, commonly referred to as Part III, is a nine-month taught masters course in mathematics. It is excellent preparation for mathematical research and it is also a valuable course in mathematics and 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, or whether they applied through the Applied Mathematics (MASA), Pure Mathematics (MASP), Mathematical Statistics (MASS), or Theoretical Physics (MASTH) application stream.

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7 courses also advertised in the Department of Applied Mathematics and Theoretical Physics

From the British Antarctic Survey

This PhD course takes place under the joint supervision of a research scientist at the British Antarctic Survey (BAS) and a University supervisor. Students may be based at BAS but will be registered for their degree with one of the partnering departments: Archaeology & Anthropology, Land Economy, Plant Sciences, Zoology, Earth Sciences, Geography and Scott Polar Research Institute, Applied Mathematics & Theoretical Physics, Chemistry, Engineering, Computer Science and Technology.

BAS welcomes enquiries from those interested in higher degrees in earth science subjects, physics, chemistry, mathematics, biology and related areas.

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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 Biosciences DTP is a four year PhD programme that aims to create highly skilled and employable people. The programme offers training across 23 University Departments/Institutes and 3 Partner Institutes providing access to a wide range of research areas related to the strategic themes of the BBSRC. We offer three types of DTP studentships:

During the programme, DTP Standard and Targeted 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).

iCASE students are not required to undertake rotations, but may do so if they feel that this training would be useful. They must undertake a placement with their Industrial Partner for a minimum of three months and a maximum of 18 months.

Students will be expected to submit their thesis at the end of the fourth year.

Part-time study, whilst not the norm, may be viable, depending on the project, and will be considered on a case by case basis so please discuss this option with your proposed supervisor before making an application for this mode of study.

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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 research and taught elements, as well as additional training elements. The final three years consist of a PhD research project, with a student-led choice of projects offered by researchers closely associated with the CDT.

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From the Department of Physics

The MPhil in Data Intensive Science is a 10-month cross-departmental programme in the School of the Physical Sciences which aims to provide education of the highest quality at the master’s level. The programme covers the full range of skills required for modern data-driven science from the fields of machine learning and AI, statistical data analysis, and research computing.  

The course structure has been designed in collaboration with our leading researchers and industrial partners to provide students with the theoretical knowledge, practical experience, and transferable skills required to undertake world-leading data-intensive scientific research. Students will gain the broad set of skills required for scientific data analysis, covering traditional statistical techniques as well as modern machine learning approaches.  Both the theoretical underpinnings and practical implementation of these techniques will be taught, with the later aspect including training on software development best practice and the principles of Open Science. The course also aims to provide students with direct experience applying these methods to current research problems in specific scientific fields. Students who have completed the course will be equipped to undertake research on data-intensive scientific projects. Beyond academic disciplines, students will be well prepared for a career as a data science professional in a broad range of commercial sectors.

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From the Department of Pure Mathematics and Mathematical Statistics

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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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 on all aspects of numerical simulation including high-performance scientific computing and advanced numerical methods, it produces postgraduates 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 Colm-cille Caulfield
Head of Department

  • 55 Academic Staff
  • 90 Postdoctoral Researchers
  • 315 Graduate Students

http://www.damtp.cam.ac.uk/

Research Areas