math phd at cambridge

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PhD in Pure Mathematics and Mathematical Statistics

University of cambridge, cambridge, united kingdom, # 2 qs subject rankings, 36 months program duration, 48,451 gbp tuition fee/year, 16 may, 2024 application deadline, program overview, main subject.

Mathematics

Study Level

Admission requirements, exam scores, important dates, application.

Applicants for this course should have achieved a UK First class Honours Degree.

The usual minimum entry requirement is a first-class honours degree, awarded after a four-year course in mathematics or mathematics/statistics, or a three-year degree together with a one-year postgraduate course in those areas. Part III (MMath/MASt) of the Mathematical Tripos provides such a course and many of the students in DPMMS have come through this route. The others have usually completed at least a comparable four-year undergraduate course, and many have postgraduate experience. Entry is competitive and a higher level of preparation may be required.

• Candidates are required to submit references or letter(s) of recommendation for acceptance

Tuition fee and scholarships

Tuition fee, scholarships, domestic students, international students.

One of the important factors when considering a master's degree is the cost of study. Luckily, there are many options available to help students fund their master's programme. Download your copy of the Scholarship Guide to find out which scholarships from around the world could be available to you, and how to apply for them.

In this guide you will find:

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QS WUR Ranking By Subject

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Department of Applied Mathematics and Theoretical Physics

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• DAMTP History
• Professor George Batchelor
• Professor David Crighton
• Related Institutions
• Research at DAMTP
• Undergraduate and Postgraduate Study
• PhD in Applied Mathematics and Theoretical Physics
• PhD in Mathematics of Information
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• DAMTP Administration

Research in DAMTP is loosely organised into eight broad subject areas:

Applied and Computational Analysis

• Astrophysics

Fluid and Solid Mechanics

Mathematical biology.

• Quantum Information
• High Energy Physics

General Relativity and Cosmology

The boundaries between the areas are not rigid and evolve with time. Many members of staff contribute to more than one area and this is regarded as a key factor in the continuing success of DAMTP. Research in each of the subject areas involves collaboration with strong groups nationally and internationally, and participation in numerous interdisciplinary projects and programmes.

Applied and Computational Analysis (ACA) at DAMTP spans a wide range of themes in partial differential equations, numerical analysis, dynamical systems and integrable systems. Its underlying organising principle is an inquiry into issues of interest in applications of mathematics and forging tools and methodology that are relevant in applications.

• Cantab Capital Institute for the Mathematics of Information
• Numerical Analysis
• Cambridge Centre for Analysis (Doctoral Training Centre)
• Cambridge Image Analysis
• Applied Functional and Harmonic Analysis
• Professor Colm-Cille Patrick Caulfield
• Professor Nilanjana Datta
• Professor Hamza Fawzi
• Professor Anders Christian Hansen
• Professor Adrian Kent
• Professor Paul Linden
• Professor Nigel Peake
• Professor Carola Bibiane Schönlieb
• Professor Alexei Shadrin
• Professor Edriss S. Titi

Researcher:

• Dr Kweku Abraham
• Dr Ander Biguri
• Zhongying Deng
• Moshe Eliasof
• Dr Julian Gilbey
• Dr Michael Roberts
• Dr Bogdan Roman
• Dr Ferdia Sherry
• Dr Orsola Rath Spivack
• Shengding Sun
• Mary Chriselda Antony Oliver
• Georgios Batzolis
• Simon Carlson
• Willem Diepeveen
• Holly Houliston
• Daniel Kreuter
• Wallace Eugene Peaslee
• Christina Runkel
• Jan Stanczuk
• Hong Ye Tan
• Samuel Tull
• Lipei Zhang

Professional and Support Staff:

• Mrs Zvezda Woodhouse

Emeritus Staff:

• Professor Thanasis Fokas
• Professor Arieh Iserles
• Josh Bannister
• Dr Anna Breger
• Dr Priscilla Canizares
• Ms Tamara Großmann
• David Oliver Cortadellas
• Dr Philipp Vieweg

College Teaching Officer:

• Dr Anthony Charles Lewis Ashton

• Professor Henrik Nils Latter
• Professor Gordon Ogilvie
• Professor Roman Rafikov
• Dr Nicole Shibley
• Dr Mattias Brynjell-Rahkola
• Dr Giulio Del Zanna
• Roger Pierre Dufresne
• Dr Loren E Held
• Thomas Jannaud
• Samuel Turner
• Joshua Jonathan Brown
• Amelia Jane Cordwell
• Thomas Daggitt
• Stan DeLaurentiis
• Eva Deliporanidou
• Nathan Malo Titouan Magnan
• Matt Roberts
• Professor Douglas Gough
• Professor Helen Elizabeth Mason
• Professor Michael McIntyre
• Professor H.Keith Moffatt
• Professor John Papaloizou
• Professor Michael Richard Edward Proctor

This extended group addresses the science of atmosphere, cryosphere, ocean and solid Earth. DAMTP has been active in these areas since its formation in 1959, largely through staff who combine interests in fundamental fluid or solid mechanics with geophysical applications. As the relevant fundamental areas have matured DAMTP staff have naturally broadened their research further into application areas or, in some cases, have played major roles in establishing new 'fundamental' areas, such as the fluid mechanics of solidification and freezing.

• Atmosphere-Ocean Dynamics
• Theoretical Geophysics
• Professor Peter Howard Haynes
• Dr Duncan Robin Hewitt
• Professor John Ronald Lister
• Professor Jerome Neufeld
• Professor John Ryan Taylor
• Professor Michael Grae Worster
• Dr Amir Atoufi
• Dr Alison Donna Ming
• Kasia Warburton
• Joséphine Anselin
• Emma Bouckley
• Isaac P. Brown
• Matt Davison
• Jennifer Dingwall
• Shi-Wei Jian
• Charles Powell
• Elvinas Ribinskas
• Jago Strong-Wright
• Wren Stuart
• Wenzhong Wang
• Dr Mark Hallworth
• Professor Herbert Huppert
• Zoe Gillett
• Chris Warner

The research of this large group extends through fluid mechanics, granular flow and solid mechanics, and an extremely wide range of applications. Members of this group are active in experimental work in the GK Batchelor Laboratory. Overall the main approach to solving scientific and industrial problems is to seek physical understanding through construction and (often asymptotic) analysis of the simplest mathematical model that is consistent both with the laws of physics and with experimental observation.

• Astrophysical Fluid Dynamics
• Biological Physics & Mechanics
• Environmental and Industrial Fluid Dynamics
• G.K. Batchelor Laboratory
• High-Reynolds-Number Fluid Flow
• Quantum Fluids
• Soft Matter
• Solid Mechanics
• Waves Group
• Professor David Abrahams
• Dr Ronojoy Adhikari
• Professor Natalia Berloff
• Professor Michael Cates
• Professor Stuart Bruce Dalziel
• Professor Ray Goldstein
• Professor Robert Jack
• Professor Rich Kerswell
• Professor Eric Jean-Marie Lauga
• Professor Ben Simons
• Dr Lorna Jane Ayton
• Dr Rajesh Bhagat
• Dr Jane Chui
• Dr George Fortune
• Dr Rosalba Garcia Millan
• Siddharth Ghosh
• Dr Steph Höhn
• Dr Catherine Kamal
• Dr Adrien Lefauve
• Dr Kyriacos Leptos
• Dr Guangyao Li
• Dr Ivan Lobaskin
• Dr. Sarah A.M. Loos
• Matthew Nethercote
• Dr Adriana Irma Pesci
• Mr Nicolaos Petropoulos
• Dr Praneet Prakash
• Dr Maria Cristina Rodriguez Rivero
• James Douglas Shemilt
• Calum Brown
• Gergely Buza
• Rami Cassia
• Rosie Cates
• James Cummins
• Filippo De Luca
• Alistair Hales
• Joel Hochstetter
• Joseph Holey
• Pyae Hein Htet
• Laura Irvine
• Airat Kamaletdinov
• Zhixuan Liu
• Arnab Mallik
• Benjamin McMillan
• Shiza Batool Naqvi
• Balázs Németh
• Stefan Nixon
• Mohammadreza Noormandipour
• William Oliver Oxley
• George Poole
• Janik Schüttler
• John William Severn
• Anand Srinivasan
• Marvin Syed
• Marco Federico Vona
• Richard, Zhipeng Wang
• Andrew Zhou
• Professor Edward John Hinch
• Professor John Martin Rallison
• Professor Peter Wadhams
• Tal Agranov
• Dr Mingnan Ding
• Dario Klingenberg
• Dr John Denis Sherwood
• Michael te Vrugt
• Mohamed Warda
• Robert Hunt

Research areas include biomechanics, biological physics, epidemiology and computational neuroscience. Part of the group plays a major role in the CCBI which is a recent cross-School initiative, hosted in DAMTP, to bring together the exceptional strengths of Cambridge in medicine, biology, mathematics and the physical sciences.

• Computational Biology
• Disease Dynamics
• Machine Learning and Artificial Intelligence
• Cambridge Centre for AI in Medicine (CCAIM)
• Professor Stephen John Eglen
• Professor Julia Rose Gog
• Professor Gos Micklem
• Professor Mihaela van der Schaar
• Dr Yuan Huang
• Yichen (Kelly) Chen
• Maria Alegria Gutierrez Guillen
• Zhiyuan Desmond Lai
• Professor Timothy John Pedley
• Mr Alex Dunn
• Dr Petra Klepac

Activities of this group are focused in the CQIF. Research topics include quantum cryptography (a particular strength), quantum computing algorithms, quantum information theory, quantum control, and modelling the implementation of quantum computers in physical systems.

• Professor Benjamin Beri
• Angela Capel Cuevas
• Professor Frank Verstraete
• Davi De Castro Silva
• Aditya Jain
• Dr Damián Pitalúa-Garcia
• Dr Subhayan Roy Moulik
• Dr Sergii Strelchuk
• Carolin Christina Wille
• Bjarne Bergh
• Mitchell Chiew
• George Cowperthwaite
• Joshua Cudby
• Samuel Fedida
• Oliver O'Brien
• Wilfred Salmon
• Satvik Singh
• Professor Richard Jozsa
• Dr Boris Groisman

This is one of two large groups in theoretical physics, the other being the General Relativity and Cosmology group. Advances on several fronts have led to a breakdown of the historical divisions between the two groups and several staff members are members of both. The group is active in Particle Physics Phenomenology, Quantum Field Theory, String Theory, and Lattice Field Theory. (Particle physics related to the Big Bang and very early universe are covered under General Relativity and Cosmology.)

• Professor Ben Allanach
• Dr Alejandra Castro Anich
• Professor Nicholas Dorey
• Professor Maciej Dunajski
• Dr Jonathan Mark Evans
• Professor Sean Alexander Hartnoll
• Professor Enrico Pajer
• Professor Harvey Reall
• Professor Jorge Eduardo Pinto Santos
• Professor David Benjamin Skinner
• Professor David Michael Addis Stuart
• Professor Christopher Thomas
• Professor David Tong
• Professor Maria Ubiali
• Dr Aron Wall
• Professor Matthew Bowen Wingate
• Dr Hannah Banks
• Mr Mark Costantini
• Dr Marine De Clerck
• Dr Jackson Fliss
• Dr Nico Gubernari
• Mr Elie Hammou
• Dr Bob Knighton JR
• Dr Nelson Lachini
• Dr Luca Mantani
• Dr Rishi Talwar Mouland
• Dr David Wilson
• Dr Bernardo Zan
• Santiago Agüi Salcedo
• Matthew John Blacker
• Robert Bourne
• Alex Colling
• Maxime Jean Hubert Gadioux
• Simon Heuveline
• Christopher Hughes
• Khoi Le Nguyen Nguyen
• Eetu Aleksi Loisa
• Nathan Matthew McStay
• Manuel Morales Alvarado
• Veronica Pasquarella
• Mario Ramos Hamud
• Paul Luis Röhl
• Ayngaran Thavanesan
• Gonzalo Villa
• Ms Amanda Stagg
• Professor Anne Christine Davis
• Professor Ian Drummond
• Professor Michael Green
• Professor Ronald Raymond Horgan
• Professor Peter Landshoff
• Dr Alan James Macfarlane
• Professor Nicholas Stephen Manton
• Professor Hugh Osborn
• Professor Malcolm John Perry
• Professor John Taylor
• Professor Paul Kingsley Townsend
• Dr Ruth Margaret Williams
• Dr Ben Gripaios
• Dr Alex Mitov
• Sir Martin Rees
• Dr Watse Sybesma
• Dr Ron Reid-Edwards

The interests and membership of this large group overlap with those of the High Energy Physics group. It hosts the COSMOS supercomputer, a national facility dedicated to studies of early Universe physics and the new Centre for Theoretical Cosmology (CTC). Despite its infancy the CTC has already hosted several conferences and workshops, including 'The Very Early Universe; 25 years on'. The group is active in numerical relativity, supergravity, discrete gravity, M-theory/string theory and cosmology.

• Relativity & Gravitation
• Cambridge Centre for Theoretical Cosmology (CTC)
• Professor Anthony David Challinor
• Dr Miles Cranmer
• Dr James Robert Fergusson
• Professor Jonathan Gair
• Professor Edward Paul Scott Shellard
• Professor Blake Sherwin
• Professor Ulrich Sperhake
• Thomas Colas
• Daniela Cors Agullo
• William Richard Coulton
• Dr Amelia Louise Drew
• Dr Carlos Duaso Pueyo
• Dr Fiona McCarthy
• Dr Francesco Muia
• Dr Isobel Romero-Shaw
• Owain Salter Fitz-Gibbon
• Dr Zvonimir Vlah
• Dr Dong-Gang Wang
• Irene Abril Cabezas
• David Gwilym Baker
• William Biggs
• Johnnie Crump
• Iain Davies
• Yaniv Donath
• Carmen Embil Villagra
• Tamara Evstafyeva
• Gerrit Simon Farren
• Ericka Ann Florio
• Tamanna Jain
• Istvan Kadar
• Yoann Levis Launay
• Mang Hei Gordon Lee
• Gareth Marks
• Ciaran Arthur McCulloch
• Alexander Dimitri Miranthis
• Seppe Staelens
• Petar Suman
• Monica Tapia del Moral
• Bowei Zhang
• Juliana Kwan
• Mr James Parke
• Dr Peter D'Eath
• Professor Gary Gibbons
• Alexander Soloviev
• Dr Yoav Zigdon
• Irena Borzym
• Matthias Doerrzapf
• Peter O'Donnell

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Cambridge centre for data-driven discovery, currently advertised phd studentships.

• The majority of current PhD studentships are listed on the  University's Jobs site
• For a full list of departments and faculties at the University, visit this page where you can learn more about the research interests within each department
• To find academics you might like to work with, use our directory

Graduate Admissions

The  Graduate Admissions  office provides a range of information on postgraduate programmes at Cambridge, along with a step-by-step guide to the application process. It is advisable to start researching funding opportunities at least a year before your course begins.

MPhil and PhD course relevant to data science - from across University of Cambridge

Please visit the relevant pages and contact the relevant education provider if you have queries. You should pay particular attention to the entry requirements and guidance for applicants there.

MPhil in Machine Learning and Machine Intelligence - an eleven month full-time programme offered by the Machine Learning Group, the Speech Group, and the Computer Vision and Robotics Group in the Cambridge University Department of Engineering.  The course aims to teach the state-of-the-art in machine learning, speech and language processing, and computer vision; to give students the skills and expertise necessary to take leading roles in industry and to equip them with the research skills necessary for doctoral study at Cambridge and other universities.

PhD programme in Advanced Machine Learning - The Machine Learning Group is based in the Department of Engineering, and encourages applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. 

Cambridge Centre for AI in Medicine - Cambridge Centre for AI in Medicine (CCAIM) is a multi-disciplinary centre established by the University of Cambridge in 2020 to develop pioneering AI machine learning (ML) technologies that will transform biomedical science, medicine and healthcare. PhD studentships are oten available, please check their website for details.

SynTech Centre for Doctoral Training - EPSRC Centre for Doctoral Training in Next Generation Synthetic Chemistry Enabled by Digital Molecular Technologies. An interdisciplinary cohort-driven programme to produce the next generation of molecule making scientists by combining Synthetic Chemistry, Chemical Engineering, Engineering, Machine Learning and Artificial Intelligence.

Advanced Computer Science MPhil  - The MPhil in Advanced Computer Science (the ACS) is designed to prepare students for doctoral research, whether at Cambridge or elsewhere. Typical applicants will have undertaken a first degree in computer science or an equivalent subject, and will be expected to be familiar with basic concepts and practices. The ACS is a nine–month course which starts in early October and finishes on 30 June. It covers advanced material in both theoretical and practical areas as well as instilling the elements of research practice.

Application of Artificial Intelligence to the study of Environmental Risks MRes and PhD - The UKRI Centre for Doctoral Training in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) are no longer recruiting students for further intakes. 

Postgraduate Study in Mathematics - Various postgraduate courses of a mathematical nature are available at the University of Cambridge, including both taught courses and research degrees.

Mathematics of Information PhD  - This cutting-edge training Centre in the Mathematics of Information produces a new generation of leaders in the theory and practice of modern data science, with an emphasis on the mathematical underpinnings of this new scientific field. The Cambridge Mathematics of Information (CMI) PhD is a four-year course leading to a single PhD thesis.

Centre for Scientific Computing MPhil and PhD  - The MPhil programme on Scientific Computing is offered by the University of Cambridge as a full-time course which aims to provide education of the highest quality at Master’s level. A common route for admission into our PhD programme is via the Centre’s MPhil programme in Scientific Computing.

Part III Mathematics  - Part III is a 9 month taught masters course in mathematics.  It is an 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. There are over 200 Part III (MASt and MMath) students each year; almost all are in their fourth or fifth year of university studies. 

School of Clinical Medicine Graduate Training Office - Prospective students interested in pursuing a graduate degree course in a subject area related to clinical medicine at the University of Cambridge should consult the School’s individual departmental websites for detailed information about the courses which they run and the University’s Graduate Admissions website for information on the application process and on funding opportunities.

Centre for Doctoral Training in Data, Risk And Environmental Analytical Methods  - The CDT embraces a wide range of world-leading Doctoral research in the area of Big Data and Environmental Risk Mitigation. The CDT research underway seeks to utilise emerging technologies, techniques and tools, to more accurately monitor the environment, enabling cutting edge research. To provide end-users with more integrated information at improved temporal and spatial resolutions to deliver solutions to environmental challenges (both acute and long- term). Funded by  NERC  (the Natural Environment Research Council, NERC Ref: NE/M009009/1), the DREAM (Data, Risk and Environmental Analytical Methods) consortium is made up of Cranfield, Newcastle, Cambridge and Birmingham universities.

Centre for Doctoral Training in Data Intensive Science  - The Cambridge CDT in Data Intensive Science is an innovative, interdisciplinary centre, distributed between the Department of Physics (Cavendish Laboratory), Department of Applied Mathematics and Theoretical Physics (DAMTP), Department of Pure Mathematics and Mathematical Statistics (DPMMS) and the Institute of Astronomy (IoA).

MPhil in Data Intensive Science - This course aims to take science graduates and to prepare them for data intensive research careers by providing advanced training in three key areas – Statistical Analysis, Machine Learning, and Research Computing – and their application to current research frontiers.

Cambridge Digital Humanities - The MPhil provides the opportunity to specialise in a chosen subject area as well as an advanced level introduction to DH approaches, methods and theory. The course provides critical and practical literacy, the chance to advance an extant specialization by re-contextualizing it in relation to advanced theoretical work, and the chance to develop as a DH scholar.

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science and AI. C2D3 is an Interdisciplinary Research Centre at the University of Cambridge.

• Supports and connects the growing data science and AI research community 
• Builds research capacity in data science and AI to tackle complex issues 
• Drives new research challenges through collaborative research projects 
• Promotes and provides opportunities for knowledge transfer 
• Identifies and provides training courses for students, academics, industry and the third sector 
• Serves as a gateway for external organisations 

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We request an absolute minimum of six weeks notice before funder deadlines to decide whether or not to support independent (URF/ERC/EPSRC etc) fellowship applications; candidates who give more notice may be at an advantage.  If you are interested, contact the HoD as early as possible. Consideration of whom to support, by our Strategy & Research committee, will require a CV, a draft research statement (including indications of any additional posts, either PhD students or postdocs, to be applied for as part of the Fellowship) and an indication of who would be your host / mentor within DPMMS. Your mentor should be a permanent member of faculty, have already agreed to act, and be ready to provide the S&R committee with a letter of support.

DPMMS has a large number of faculty, postdocs and graduate students, with active research groups across the spectrum of mathematics. Strengths include:

• algebra (group theory, representation theory);
• algebraic geometry (higher-dimensional algebraic geometry, algebraic cycles, abelian varieties, mirror symmetry, geometric aspects of representation theory);
• analysis (geometric analysis, PDEs, mathematical relativity, discrete analysis);
• category theory (logic, proof theory, topos theory, higher-dimensional category theory)
• combinatorics (random structures, combinatorial number theory, Ramsey theory, graph theory)
• differential geometry (dynamics, low-dimensional topology and knot theory, gauge theory, Riemannian geometry, symplectic topology);
• number theory (algebraic number theory, Iwasawa theory, computational number theory);
• probability and statistics (including financial mathematics, operational research, stochastic methods, transport and networking problems).

More detailed information can be found on other departmental webpages. If you have interests in these areas, particularly in the areas of expertise of departmental members, consider coming to Cambridge for a postdoc. (Related research groups at Imperial College, London and at Oxford are close, and there are close links -- including shared participation in some seminars -- with these departments, which adds to the richness of the local mathematical community.) There are various kinds of postdoc available, suitable for people at different levels, and requiring different application procedures. Some more information on each is available below.

• HERCHEL-SMITH POSTDOCTORAL RESEARCH FELLOWSHIPS: A Fellowship in Mathematics is advertised as part of a University wide scheme. We expect to appoint one or more positions each year. The postdocs are tenable for 3 years and are on a competitive salary scale. The positions carry no teaching obligations, and are purely intended for research, although opportunities to give Masters-level or graduate-level courses are available. Applications from all areas of pure mathematics will be considered. Applicants should be towards the end of their PhD or within the first 2-3 years of postdoctoral research. Further particulars will be given on the departmental vacancies page when applications are open, which is typically around late October.
• COLLEGE RESEARCH FELLOWSHIPS: These are prestigious postdocs, funded directly by the Cambridge Colleges. Holders of the positions are fully integrated into DPMMS, with office space in the department as well as their college. The precise details of these positions vary from college to college, and their availability varies from year to year. Typically, colleges offer up to 4 positions, for a tenure of 3 or 4 years. All positions are research positions, with few or minimal teaching obligations; the positions are associated with membership of a college, and typically come with accommodation and other perquisites. In some but not all cases there may be criteria of eligibility (e.g. citizen of an EU member state, or graduand of a UK university, etc). All such positions are advertised in the Cambridge University Reporter: www.admin.cam.ac.uk/reporter/current/weekly/ ; check regularly to see if there are openings in Mathematics. Note that although these positions are competitive across subjects, mathematicians are often successful: in a typical year there are 3 or more pure mathematicians appointed to such positions from the various colleges. For a further view see Professor Körner's unofficial
• Guide for Mathematicians Seeking a Cambridge Research Fellowship
• 1851 ROYAL COMMISSION RESEARCH FELLOWSHIPS: 2-year postdoctoral fellowships in mathematics and biological/physical sciences, which can be held at any UK university. Applicants should recently have completed or be about to complete a PhD: there are strict eligibility criteria (UK or Commonwealth national, or citizen of Ireland or Pakistan). Further information and application deadlines can be found here.
• MARIE-CURIE POST-DOCTORAL FELLOWSHIPS: 2 year research-only postdoctoral positions, funded by the EU (to a generous salary), available to EU nationals to work in a country which is neither the country of their PhD nor their home country. Open calls for applications -- which are often but not always present -- can be found with some effort via the website ; follow links for Individual Researchers. Marie-Curie grant applications are made jointly with the department, and are directly linked to the research of a specific member of faculty. The first stage in applying is to make contact with such a potential supervisor and discuss possible projects and establish whether or not there is support for and scope for a plausibly successful application.
• ROYAL SOCIETY UNIVERSITY RESEARCH FELLOWSHIPS: Unique 8 year research-only fellowships. These are suitable for outstanding young mathematicians who have completed their PhD and already done one postdoc. Applications must have emphatic support from DPMMS; if you are interested in applying, contact a member of the department in a related subject. Further information is available from their website :     The deadline is typically in very early November of the preceeding year. Results are only available in April, but who else is going to give you 8 years money with no obligations?

POSSIBILITIES NEARBY: Imperial College London is only an hour from Cambridge, also has outstanding research groups in various fields (including the geometry group led by Simon Donaldson), and we often travel to and from one another's seminars. Oxford is slightly further but by no means inaccessible. Why not try applying there too? More information can be found on the websites https://www.imperial.ac.uk/mathematics/research/opportunities/ , and www.maths.ox.ac.uk/ . Example opportunities include:

• Chapman Fellowships at Imperial
• Colleges at the University of Oxford also offer Junior Research Fellowships, details are usually available via the Gazette www.ox.ac.uk/gazette/ .

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City University of Hong Kong (CityUHK) and the University of Cambridge signed several Memoranda of Understanding (MoUs) to foster academic and research collaboration in 2024.

Not only a partner of CSCI’s internship schemes, the University of Cambridge is also the groomer of our outstanding students.

We are proud that Mr. Sam Cheng Chun Wun , a CityUHK BSc in Computing Mathematics (BSCM) local graduate, received a four-year full scholarship of HK$2 million to pursue his PhD study in Mathematics of Information at the University of Cambridge. Currently, he is a PhD student under the co-supervision of Professor Carola-Bibiane Schönlieb and Dr. Angelica I. Aviles-Rivero, at the Cambridge Image Analysis Group within the Department of Applied Mathematics and Theoretical Physics.

Sam graduated with First-Class Honours from BSCM programme at CItyUHK in 2023. When studying at CityUHK, Sam was on the Dean’s List thrice. With support from the College of Science (CSCI), he participated in the Overseas Internship Scheme from May to Aug 2022 and worked on a summer research project about Neural Ordinary Differential Equations (NODEs) and medical image segmentation at the University of Cambridge. He achieved Top Quartile results in the 2022 Simon Marais Mathematics Competition (SMMC), partnering with Toromanovic Jovan (a student of BSc in Computer Science) and won the “Pair- Best-in-University Prizes, East Division” Prize in the competition.

City University of Hong Kong's Mathematics Department has played a crucial role in shaping my academic and personal growth,” Sam shared. The department has provided him countless opportunities and a supportive environment to develop skills and explore interests in mathematics. “Through their invaluable guidance, I have had the opportunity to participate in various STEM internships and overseas summer research at prestigious institutions such as the University of Cambridge.” He said.

Sam is the first BSc Computing Mathematics graduate admitted with 4-year full scholarship for PhD study at the University of Cambridge since 1994. Congratulations!

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PhD in Human-Inspired Artificial Intelligence

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This exciting PhD in Human-Inspired Artificial Intelligence will train the next generation of AI researchers, technologists, and leaders in the development of human-centred, human-compatible, responsible and socially and globally beneficial AI technologies. The course offers research training in areas such as fundamental human-level AI, social and interactive AI, cognitive AI, creative AI, health and global AI, and responsible AI. Students will be educated in an interdisciplinary environment where they can get access to expertise not only in the technical but also human, ethical, applied and industrial aspects of AI.

This programme is distinct from other PhD programmes in that it takes a strongly interdisciplinary and cross-disciplinary approach to technical AI. It will be based at the Centre for Human-Inspired Artificial Intelligence (CHIA) within the Institute for Technology and Humanity (ITH) where PhD students will have access to both a large community of scholars and students tackling similar questions and to the active research events programme that constitutes a key part of CHIA’s work. The course addresses the broader need for experts equipped to develop more responsible and human-centred AI as academia, industry, government and non-profit sectors increasingly recruit AI specialists and is a logical next step for students moving through AI-related master’s programmes and wishing to specialise in human-inspired AI. The interdisciplinary nature of human-inspired AI means that the programme will involve working closely also with other units of the University, including co-supervision arrangements, access to research seminars, and access to facilities.

The PhD in Human-Inspired AI aims to equip students with the skills and knowledge to contribute critically and constructively to research in human-inspired AI. It introduces students from diverse backgrounds to research skills and specialist knowledge from a range of academic disciplines and provides them with the opportunity to carry out focused research under close supervision by domain experts at the University.

The programme will train the next generation of researchers and leaders in AI by

• providing them with educational infrastructure and interdisciplinary research environment and world-leading training in human-inspired AI,
• providing them with the critical tools to engage with the forefront of academic knowledge, methods and applications in this area,
• developing the advanced skills and abilities to identify, approach and address practical interdisciplinary research challenges,
• supporting students to develop a broad and deep understanding of the technical, ethical, applied and human aspects of AI, 
• developing the ability and initiative to identify, address and approach relevant and complex challenges across sectors and society.

The course will benefit  

• students wanting to engage with human-inspired AI by enabling them to hone critical, methodological and technical skills, develop new approaches and test them out, and specialise,
• students locating themselves in other home disciplines who wish to develop advanced projects including CHIAs approaches and orientations, 
• students entering into or returning to careers in academia, tech industry, and other sectors by giving them the advanced skills, critical perspectives, and methodological insights to pursue these pathways.

Learning Outcomes

Knowledge and Understanding

By the end of the PhD programme our graduates will demonstrate:

• The ability to create and interpret new knowledge, through original research or other advanced scholarship of a quality to satisfy peer review, extend the forefront of the discipline, and merit publication.
• The general ability to conceptualise, design and implement a project for the generation of new knowledge, applications or understanding at the forefront of human-inspired AI, and to adjust the project design in the light of unforeseen problems.
• A detailed understanding of applicable techniques for cross-disciplinary research and advanced academic enquiry in the field of human-inspired AI
• The ability to make informed judgements on complex issues in human inspired AI, often in the absence of complete data.
• A critical perspective on the governance and ethical challenges that arise from applications of human-inspired AI and how these sit within and interact with wider society. 
• A systematic acquisition and understanding of a substantial body of knowledge in relation to the history, methods, and applications of human-inspired AI.

Skills and other attributes

Graduates of the course will be able to:

• Continue to undertake pure and/or applied research and development at an advanced level, contributing substantially to the development of new techniques, ideas or approaches.
• Communicate their ideas and conclusions clearly and effectively to specialist and non-specialist audiences.
• Contribute constructively within national, international and cross-disciplinary environments.
• Transfer skills and qualities acquired during the programme to successfully engage in employment requiring the exercise of personal responsibility and largely autonomous initiative in complex and unpredictable situations, in professional or equivalent environments.

Employability

Students of the programme will graduate with a formal qualification in the rapidly expanding area of AI. The emphasis is on human-inspired AI. The combination of specialist, technical expertise in AI and cross-disciplinary approaches involving a wide range of human-centric disciplines means that our doctoral graduates will be uniquely qualified in the sector. The PhD will, therefore, put them in a strong position to pursue careers in a variety of academic and non-academic settings, for example organisations and consultancies in diverse sectors such as tech, health, environment, education, journalism, civil service among others.

For those intending to continue into an academic career, the course will equip them with the skills, experience and qualification for applying for a postdoctoral research position.

For Cambridge students applying to continue from the MPhil to a PhD, students must achieve a pass in the MPhil by Thesis or an overall distinction in the MPhil by Advanced Study.

All applications are judged on their own merits, and students must demonstrate their suitability to undertake doctoral-level research.

The Centre for Human-Inspired Artificial Intelligence (CHIA) will hold an online webinar 9:00-9:45am on 4 November 2024.  Please see the  CHIA website  for information on how to register for this event. 

The Cambridge University Postgraduate Virtual Open Day usually takes place at the beginning of November.  It's a great opportunity to ask questions to admissions staff and academics, explore the Colleges virtually, and to find out more about courses, the application process and funding opportunities. Visit the  Postgraduate Open Day  page for more details.

See further the  Postgraduate Admissions Events  pages for other events relating to Postgraduate study, including study fairs, visits and international events.

Key Information

3-4 years full-time, 4-7 years part-time, study mode : research, doctor of philosophy, institute for technology and humanity, course - related enquiries, application - related enquiries, course on department website, dates and deadlines:, michaelmas 2025.

Some courses can close early. See the Deadlines page for guidance on when to apply.

Funding Deadlines

These deadlines apply to applications for courses starting in Michaelmas 2025, Lent 2026 and Easter 2026.

Similar Courses

• Human-Inspired Artificial Intelligence MPhil
• Future Infrastructure and Built Environment EPSRC CDT PhD
• Conservation Leadership MPhil
• Chemistry MPhil
• Sensor Technologies and Applications EPSRC CDT PhD

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