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Machine learning and computer vision
- Accelerating Data Science and Artificial Intelligence (ADA)
Dense 3D face modelling and reconstruction
Machine learning is the science of constructing algorithms that learn from data and in the case of computer vision, to image and video data.
The proliferation of data and the availability of high performance computing makes this a fertile and very applicable area of research. It draws on ideas in computer science, statistics and applied mathematics, together with biologically inspired paradigms such as neural computation.
Machine learning research at Exeter spans the range of data, applications and methodologies: from kernel methods to deep neural architectures and reinforcement learning applied to both continuous and discrete, graph-based data.
The group collaborates with industrial partners and with the Impact Lab , and contributes strongly to the University’s membership of the Alan Turing Institute .
Group members
- Key Contact: Professor Richard Everson - Professor of Machine Learning (Research Lead)
- Prof Alberto Arribas - Associate Professor
- Dr Jacqueline Christmas - Senior Lecturer
- Dr Fabrizio Costa - Lecturer
- Dr Leon Danon - Senior Lecturer
- Dr Anjan Dutta - Lecturer
- Professor Jonathan Fieldsend - Professor
- Dr Dmitry Kangin - Research Fellow
- Professor Ed Keedwell - Professor
- Dr Lorenzo Livi - Lecturer
- Dr Chunbo Luo - Senior Lecturer
- Dr Sareh Rowlands - Lecturer
- Dr Wenjie Ruan - Senior Lecturer
- Prof Hywel Williams - Associate Professor
- Dr Johan Wahlström - Lecturer
Former members
- Dr Nicolas Pugeault - Lecturer
- Dr Anastasios (Tassos) Roussos - Lecturer
Research projects
For a full list of current and recent research projects within this strand of our work, please see our research projects page .
Visual attention and pre-attentive perception
How much of driving is pre-attentive? Quite a lot, it appears. For an experienced driver, the act of driving requires little attention, allowing for extended periods of driving, while at the same time having a conversation, looking for directions or daydreaming. It is precisely because little attention is necessary that drivers' inattention is such a common cause of accident.
Research in collaboration with the University of Surrey has been investigating exactly how much of a driver’s actions could be explained by pre-attentive perception only [1]. This research employed a computational model of pre-attentive perception with machine learning to mimic the driver’s behaviour. Experiments showed that up to 80% of steering and braking actions of a driver can be fully explained by such a pre-attentive model of perception. Moreover, this model was able predict the driver's steering accurately up to a full second before they started turning the wheel.
Ongoing research is investigating the full role of attention in dynamic tasks such as driving, and how a successful attentional strategy can be learnt from experience. In particular, recent experiments have allowed us to explain what is learnt by Deep Saliency models of visual attention [2].
This research is funded by the EPSRC under project DEVA: Autonomous Driving with Emergent Visual Attention.
- Pugeault N, Bowden R. (2015) How much of driving is pre-attentive? IEEE Transactions on Vehicular Technologies , volume 64, pages 1-15, article no. 12, DOI:10.1109/TVT.2015.2487826.
- He S, Pugeault N. (2017) Deep saliency: What is learnt by a deep network about saliency? 2nd Workshop on Visualization for Deep Learning at the 34 International Conference on Machine Learning , Sidney, Australia, 10th Aug 2017.
- He S, Kangin D, Mi Y, Pugeault N. (2018) Aggregated Sparse Attention for Steering Angle Prediction , International Conference on Pattern Recognition , Beijing, China, 20th-24th Aug 2018.
Autonomous control
Consider a team of robots charged with exploring an unknown environment completely autonomously. Their task will be to discover the environment’s layout, to localise themselves with respect to visible landmarks and each other, and to plan trajectories ahead.
The state of the art in robotics research provides robust methods to model a robot’s environment from vision and motion, so called SLAM (Simultaneous Localisation and Mapping), as well as for localising itself in this map using visual landmarks. Once the environment’s layout and the robot position are known, planning and navigation are also well studied problems.
What is less understood by previous research is:
- how should a robot plan its actions and motions initially in order to discover its environment most efficiently and accurately? [1]
- if we assume that the exploration can done by multiple robots simultaneously, how should these robots decide when and how to cooperate to improve exploration? [2]
In response to those questions, we have developed novel algorithms for planning optimal collaborative exploration of unknown environments by teams of autonomous robots.
This project is in collaboration with the Centre for Vision Speech and Signal Processing (CVSSP) at the University of Surrey.
Video: Humans can localise without LiDAR, can robots?
- Mendez Maldonado, Oscar, Hadfield, Simon, Pugeault, Nicolas and Bowden, Richard (2016) Next-best stereo: extending next best view optimisation for collaborative sensors. British Machine Vision Conference , 19-22 September 2016, York, UK.
- Mendez Maldonado, Oscar, Hadfield, Simon, Pugeault, Nicolas and Bowden, Richard (2017) Taking the Scenic Route to 3D: Optimising Reconstruction from Moving Cameras . IEEE International Conference on Computer Vision 2017 , 22-29 October 2017, Venice, Italy.
- O Mendez, S Hadfield, N Pugeault, R Bowden (2018) SeDAR-Semantic Detection and Ranging: Humans can localise without LiDAR, can robots? Proceedings of the International Conference on Robotics and Automation (ICRA'2018) , IEEE.
3D face modelling with unprecedented quality
We have recently created the most accurate digital 3D model of human faces [1,2]. We introduced the largest-scale 3D morphable model of facial shapes ever constructed, based on a dataset of more than 10,000 distinct facial identities from a huge range of gender, age and ethnicity combinations.
For this large scale facial model (LSFM), we proposed a fully automated system that establishes dense correspondences among 3D facial scans, yielding state-of-the-art results.
We are currently investigating novel methodologies that will allow us to extend our 3D face modelling so that it can also represent the 3D shapes of emotive faces with unprecedented accuracy. Towards that goal, we have recently collected a new dataset of dynamic 3D facial scans of around 5,000 individuals. This large-scale dataset includes facial scans of the participants in several different facial expressions. This data collection was done during a special exhibition and technology demonstration at the Science Museum, London.
This work is in collaboration with scientists from Imperial College London and craniofacial surgeons from Great Ormond Street Hospital and Royal Free Hospital.
Science magazine article
Science magazine featured this research project and its applications.
- J. Booth, A. Roussos, S. Zafeiriou, A. Ponniah, and D. Dunaway. A 3D Morphable Model learnt from 10,000 faces . International Conference on Computer Vision and Pattern Recognition (CVPR 2016) , Las Vegas, Nevada, USA, June 2016.
- J. Booth, A. Roussos, A. Ponniah, D. Dunaway, and S. Zafeiriou. Large Scale 3D Morphable Models , International Journal of Computer Vision (IJCV) , 2017.
Dense 3D face reconstruction from images and videos under unconstrained conditions
As part of our ongoing research, we are exploiting our highly-accurate 3D face modelling for problems of Computer Vision and Computer Graphics. Reconstructing the detailed 3D shape and dynamics of the human face has numerous applications, such as facial expression recognition, human-computer interaction, augmented reality, performance capture, computer games and visual effects, to name a few.
Despite the important advances in the related scientific fields, the existing methods have several limitations, since they can only work reliably under restrictive acquisition conditions. In our work, we seek to develop novel formulations and pioneering methodologies for dense 3D reconstruction and modelling of human faces that will be able to deal with challenging real-life image data.
We are paying particular attention to computational efficiency and to achieving unprecedented accuracy and robustness to challenging scenarios, such as low-resolution image data, severe occlusions, strong illumination changes and large intra-subject variability in facial morphology.
This work is in collaboration with scientists from Imperial College London.
- J. Booth, A. Roussos, E. Ververas, E. Antonakos, S. Ploumpis, Y. Panagakis, and S. Zafeiriou. 3D Reconstruction of "In-the-Wild" Faces in Images and Videos. Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) , Accepted for publication.
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Computational Modelling and Data Science includes researchers developing physics-based simulation techniques such as FEA and CFD, and the new simulation techniques of Engineering Data Science and AI. Applications of these technologies can be found in bioengineering and in robotics and control. Researchers in this Theme also work collaboratively with those in other Themes on simulation, virtual design and development of digital twins.
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There are often specific vacancies at both PhD and postdoctoral level - these are advertised on the University web site , and vacancies specific to the Computational Engineering group will be advertised here.
Applications from self-funded PhD students and visitors are welcomed at any time - please contact the relevant academic staff for your interests.
Group members have an excellent track record of working with companies in many areas of Engineering, spanning startups, SME and international companies, through mechanisms such as InnovateUK, KTP and various Industrial PhD/EngD projects, plus direct consultancy. If your company is interested in our work and capabilities or would like to explore how we could work together, please contact the relevant academic staff.
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Research work within this theme is delivered and supported by various centres and groups based in the Department of Engineering.
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We have 3 University of Exeter Computer Science PhD Projects, Programmes & Scholarships
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Mapping Preterm Sleep Microstructure and EEG Biomarkers for Early Neurodevelopmental Risk Assessment. MRC GW4 BioMed DTP PhD studentship 2025/26
Phd research project.
PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.
Competition Funded PhD Project (Students Worldwide)
This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.
Optical metasurfaces for multifunctional infrared spectroscopy and hyperspectral imaging
Competition funded phd project (uk students only).
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. The funding is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.
EPSRC InDustrial CDT in Offshore Renewable Energy (IDCORE)
Funded phd programme (european/uk students only).
Some or all of the PhD opportunities in this programme have funding attached. It is available to citizens of a number of European countries (including the UK). In most cases this will include all EU nationals. However full funding may not be available to all applicants and you should read the full programme details for further information.
EngD Programme
An EngD is a professional doctorate in Engineering and related subjects. The qualification is equivalent to a PhD, but involves more vocational and practice-based elements, geared towards the needs of industry. Applicants are often based within a company whilst completing their doctorate.
NERC Centre for Doctoral Training
NERC Centres for Doctoral Training conduct research and training in priority areas funded by the UK Natural Environment Research Council. Potential PhD topics are usually defined in advance. Students often receive additional training and development opportunities as part of their programme.
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Top 20 for Computer Science
20th in the times and the sunday times good university guide 2024, partner to the alan turing institute and home to exeter’s institute for data science and artificial intelligence, excellent facilities spanning a wide range of machine types and software ecosystems, courses designed to launch and develop careers for those working in or entering data and technology-driven roles, degrees in exeter.
Computer Science at Exeter
Choosing to study Computer Science at Exeter means you’ll learn from teaching that draws directly from our particular research strengths in AI, machine learning, data science, statistical modelling, high performance computing and networks, and cyber-security.
We have designed these programmes to support you in lifelong learning, so you can use your studies to accelerate or redirect your career, or progress into further academic study or a PhD.
As a Computer Science postgraduate student at Exeter, you’ll be welcomed into a tight-knit, friendly department that supports close personal contact between staff and students.
You’ll work in a highly productive and well-organised research environment, with a suite of new teaching and research laboratories in the Harrison Building. Rich in current research and taught using industry standard software and methods, you can also choose to combine your degree with modules in Strategy, Marketing, Management and Leadership.
"Computer Science at the University of Exeter provides a collaborative and inclusive environment in which students and researchers can advance their knowledge of this dynamic and exciting subject."
Professor Andrew Howes
Head of Computer Science
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PhD in Computer Science
University of Exeter, Devon
Faculty of Environment, Science and Economy
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PhD in Computer Science at the prestigious University of Exeter is a prestigious degree that offers in-depth learning in Computer Science. Being a renowned university, University of Exeter receives enough funds to ensure the best education facilities for its students across all programs. This doctorate program offered full-time primarily focuses on the practical implementation of fresh ideas through rigorous study and research. The students are encouraged to add new aspects and findings to the existing area of knowledge. PhD in Computer Science at Exon is ranked globally by estimated organisations. Such recognition speaks volumes about the course’s importance and effectiveness in the present scenario. The top-notch faculty, modern facilities, and the aura of creativity and innovation in the Exon campus is a life-changing experience for the students looking forward to kickstarting or upgrading their careers. Overall, a PhD in Computer Science at University of Exeter is an excellent opportunity to grow into a learned professional and bring new developments in the world.
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PhD research opportunities
Mathematics and Statistics brings together our internationally leading research in several areas of pure mathematics, applied mathematics and statistics, including number theory, arithmetic geometry, geophysical and astrophysical fluid dynamics, dynamical systems, control theory, statistical forecasting and uncertainty quantification. See the research group and staff pages for specific project suggestions.
To apply for PhD or other research degrees, see https://www.exeter.ac.uk/study/pg-research/degrees/mathematics/ including links to possible sources of funding. We list below some current opportunities of interest across the department.
Two Funded PhD opportunities for September 2024 (home tutition fees)
The University of Exeter’s Department of Mathematics and Statistics is inviting applications for two PhD studentships fully-funded to commence on 23 September 2024 or as soon as possible thereafter.
For eligible students the studentship will cover Home tuition fees plus an annual tax-free stipend of at least £19,237 for 3.5 years full-time, or pro rata for part-time study. The student would be based in the Department of Mathematics and Statistics in the Faculty of Environment, Science and Economy at the Streatham Campus in Exeter.
Several possible projects in mathematics and statistics are offered (with academic supervisors as named)
- Multi-step look-ahead adaptive design of computer experiments (Dr Hossein Mohammadi)
- A rapidly changing high-latitude environment: a carbon sink or a source? (Dr Mike O'Sullivan)
- Improving emulation and calibration of high-dimensional environmental models (Dr James Salter)
- Random number generation using β-encoders (Prof Tony Samuel)
- Limit theorems and mixing properties for dynamical systems with an infinite invariant measure and a non-integrable observable (Dr Tanja Schindler)
- Mathematical modelling of perception and action dynamics (Dr Piotr Slowinski)
- Data fusion in ecological modelling (Dr Oscar Rodriguez de Rivera Ortega)
Details of the projects being offered and contact information for the supervisors are listed below.
This award provides annual funding to cover Home tuition fees and a tax-free stipend. For students who pay Home tuition fees the award will cover the tuition fees in full, plus at least £19,237 per year tax-free stipend
The studentship will be awarded on the basis of merit for 3.5 years of full-time study to commence on 23 September 2024.
Entry requirements:
Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology.
If English is not your first language you will need to meet the required level as per our guidance at https://www.exeter.ac.uk/pg-research/apply/english/
How to apply
In the application process you will be asked to upload several documents.
- Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the specific project. You MUST include the title and project supervisor .)
- Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
- Names of two referees familiar with your academic work. You are not required to obtain references yourself. We will request references directly from your referees if you are shortlisted.
The closing date for applications is midnight on 16th July 2024. Interviews will be held virtually in the week commencing [19 July 2024].
Applications should be made online via this link: Award details | Funding and scholarships for students | University of Exeter
If you have any general enquiries about the application process please email [email protected] or phone +44 (0)1392 722730 or +44 (0)1392 72515. Project-specific queries should be directed to the relevant supervisor (contact details from https://mathematics.exeter.ac.uk/people/academicstaff/ ).
PhD project details
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| Hossein Mohammadi |
Today, computer simulations play an important role in various scientific fields, ranging from climate change to healthcare. Since these models are typically computationally intensive, we have access to a limited number of model evaluations. To overcome this computational challenge, we usually use a cheap-to-evaluate statistical surrogate to predict the model output. Gaussian process emulators [1, 2] are commonplace surrogates in the field of computer experiments. Building a surrogate requires a set of simulation runs, a.k.a. training data points. These points need to be chosen in a carefully designed manner to achieve maximum possible accuracy. Traditionally, training points are obtained based on a space-filling sampling scheme, which distributes them as uniformly as possible across the space. In recent years, the adaptive design of computer experiments, a.k.a. learning, has gained considerable attention [3, 4]. In an adaptive strategy, information from the emulator and previous simulation runs is used to determine the next model evaluation. This approach offers a smarter way to select future design points, allowing for more sampling in regions of interest compared to space-filling designs, which treat all regions as equally important. |
| Tony Samuel |
One of the most classical examples of a dynamical system stem from β-transformations. Namely, self-maps on [0,1] of the form x→{βx}, where β is a real number with |β|>1 and where {βx} denotes the fractional part of βx. They are the simplest family of expanding interval maps and yet give rise to a variety of intricate dynamical behaviours. When β is not an integer, these transformations lead to a beautiful representation of real numbers, i.e. non-integer base expansions, which have their origins in the works of Alexander Gelfond [1], William Parry [4] and Alfréd Rényi [5]. These β-expansions possess very different properties to their integer counterparts (e.g. binary or decimal) and continued fraction expansions. By exploiting these discrepancies Yutaka Jitsumatsu et al. [3,4] have developed a new random number generator which was verified by the National Institute of Standards and Technology (NIST) statistical test suite. This is precisely where this project will kick off, namely to explore the results of Yutaka Jitsumatsu et al. and to tackle some of the open questions posed in [3,4]. |
| Oscar Rodriguez de Rivera Ortega |
Citizen science, or public participation in scientific research, presents a significant opportunity for enhancing data availability. However, this type of information is deeply intertwined with human knowledge and behaviour, which introduce several complexities into the modelling framework. These complexities manifest in various ways, including uncertainty, sampling bias, and variations in sampling effort. |
| Mike O'Sullivan |
The high latitudes comprise of Arctic and Boreal ecosystems. They currently experience the fastest warming, largest variability (extremes), especially in the winter due to cryosphere – albedo feedbacks. As such they are the canary in the coal mine for climate change and climate-carbon cycle feedbacks, given their enormous carbon stocks above-ground in the boreal forests and below-ground in Arctic tundra permafrost soils. IPCC predicts large future changes in snow cover and permafrost, and it is unclear the magnitude impact on the main greenhouse gases (GHGs), CO2, CH4 and N2O. There is a contemporary carbon sink in the growing season, yet future projections in response to climate change are uncertain. Massive stores of organic carbon are stored in permafrost which will likely become unstable under warming, triggering multiple climate feedbacks. There is also large uncertainty around the magnitude of the C sink in boreal forests; atmospheric inversions suggest a larger sink than process-based models. It has been proposed by this team that this may be link to the representation of forest age in process-based models. We therefore urgently need to focus on disturbance and mortality processes in high-latitudes (e.g. fire, permafrost thaw, wind stress) and both in above- and below-ground carbon stocks. |
| James Salter ([email protected]) |
The overarching aims of this PhD project are to improve emulation and calibration of computer models where the model outputs are high-dimensional, with a focus on methodological development but with motivating applications from environmental models. |
| Piotr Slowinski |
Hand and eye coordination is a basic skill crucial to perform countless daily activities. Hand-eye coordination is shaped by perception and action dynamics, the way we are reacting to external stimuli (e.g., spotting notification and reaching for a mobile phone). The perception and action dynamics depends on sensory-motor and neural latencies, i.e., time it takes to notice stimulus (perception), time to process it and decide on appropriate action (processing) and time it takes to complete the movement (action). |
| Tanja Schindler (currently: ) |
We consider an ergodic system (X, μ, T) and an observable f:X→ℝ. It is one of the most classical problems in ergodic theory to study the long term behaviour of f,...,f∘︎T^n; in particular of the Birkhoff sum f+...+f∘︎T^n. Such systems are often better suited as models than iid random variables as short term dependencies can be taken into account. Some of the results are expected to be obtained mostly by a applying a combination of the methods from settings a) and b), e.g. in the case of stable laws or two-sided almost sure limit theorems. These parts of the project can be considered as a lower risk and in an optimal case give already early publishable results. Depending on the student's progress and interest there are different possibilities in which the project can continue: For instance, there are a number of number theoretic examples which exhibit this behaviour, e.g. different non-standard continued fraction expansions which could give nice applications. On the other hand, there are also a number of zero-entropy dynamical systems (like suspension flows or adding machines) and together with a non-integrable observable they fall into the above described setting - however for them even in setting a) and b) a lot less is known. |
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MPhil Computer Science
University of exeter, different course options.
- Key information
Course Summary
Tuition fees, entry requirements, similar courses at different universities, key information data source : idp connect, qualification type.
MPhil - Master of Philosophy
Subject areas
Computer Science
Course type
Our main areas of Computer Science research include Artificial Intelligence, Computer Vision, Cyber Security, Data and Network Science, Evolutionary Computing and Optimisation, High Performance Computing and Networking, and Machine Learning.
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- Alan Turing Institute, the national institute for Data Science and AI
Research overview
Our main areas of Computer Science research are:
Artificial intelligence research areas focus on social network understanding, remote sensing, human-computer interaction, cognitive science and on the philosophical foundations of artificial intelligence and computer science.
Computer vision research activities include visual attention, autonomous control, collaboration and decision strategies for cooperative robots, deep multi-modal embedding, graph neural networks etc.
Cyber security research mainly focuses on formal methods, security/safety engineering, and software engineering with the aim to build secure, reliable, resilient software and hardware systems.
Data and network science research the phenomena, intrinsic properties and real-world applications of complex networks (such as complex networks and human dynamics), which are often inspired by nature and occur in many real-world contexts including social, biological and neural networks.
Evolutionary computing and optimisation research focuses on developing evolutionary algorithms, genetic programming, hyperheuristics, swarm intelligence and multi- and many- objective versions of these for problems such as hydroinformatics, bioinformatics, optimisation under uncertainty and interactive evolution.
High performance computing and networking investigates the advanced computational and networking challenges associated with the future Internet, 5G mobile networks, cloud and edge computing, unmanned vehicles, and high performance computing.
Machine learning research at Exeter spans the range of data, applications and methodologies from kernel methods to deep neural architectures and reinforcement learning applied to both continuous and discrete, graph-based data.
UK fees Course fees for UK students
For this course (per year)
International fees Course fees for EU and international students
For all research degrees you will normally be required to have obtained or expect to obtain a first degree equivalent to at least a UK 2:1 Honours degree. You will need at least a 2:1 or its equivalent in order to be considered by any of the main funding bodies. For some programmes we also consider evidence of relevant personal, professional and educational experience.
Health Data Science MSc
London school of hygiene & tropical medicine, university of london, msc entertainment management & analytics, norwich university of the arts, msc advanced computer science, northumbria university, newcastle, msc artificial intelligence.
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The University of Exeter and Tsinghua University have launched a jointly-awarded PhD degree programme in climate and environmental sciences which supports six students to be co-supervised between Tsinghua's Department of Earth System Science and Colleges at Exeter that conduct research on earth systems and environmental sciences. Read more.
Choosing to study Computer Science at Exeter means you'll learn from teaching that draws directly from our particular research strengths in AI, machine learning, data science, statistical modelling, high performance computing and networks, and cyber-security. We have designed these programmes to support you in lifelong learning, so you can ...
Computer Science at the University of Exeter provides a collaborative and inclusive environment in which students and researchers can advance their knowledge of this dynamic subject and establish foundations for future careers. Our research-led, business-linked programmes explore the breadth of Computer Science and its applications, enabling ...
The programme structure is designed to provide the core concepts in computer science in term 1, such as algorithms and architectures, data systems, programming and security. In term 2 this foundation of knowledge will be extended to the practical domains with the introduction of ethical and governance frameworks as well as professional ...
About. This Computer Science MPhil/PhD programme from The University of Exeter will be given an up-to-date or specialised computer for daily research work. Visit the Visit programme website for more information. The University of Exeter. Exeter , England , United Kingdom. Top 1% worldwide. Studyportals University Meta Ranking. 4.1 Read 71 reviews.
PhD Computer Science. UNIVERSITY OF EXETER. Compare Different course options ... Our main areas of Computer Science research include Artificial Intelligence, Computer Vision, Cyber Security, Data and Network Science, Evolutionary Computing and Optimisation, High Performance Computing and Networking, and Machine Learning. ... based at Exeter ...
A jointly awarded Engineering Doctorate (EngD) from the Universities of Edinburgh, Exeter, Strathclyde and Swansea, offering ten places per year funded by the UK Engineering and Physical Sciences Research Council (EPSRC). Read more. Funded PhD Programme (European/UK Students Only)EngD ProgrammeNERC Centre for Doctoral Training. More Details.
FindAPhD. Search Funded PhD Projects, Programmes & Scholarships in Computer Science at University of Exeter.
We have 20 Computer Science (exeter) PhD Projects, Programmes & Scholarships. Show more Show all . More Details . Optical metasurfaces for multifunctional infrared spectroscopy and hyperspectral imaging. University of Exeter Department of Physics and Astronomy. About the project. Infrared (IR) spectroscopy reveals nearly all chemical/biological ...
Overview. Prof. Jia Hu is an Associate Professor in Computer Science at the University of Exeter. He received his PhD in Computer Science from the University of Bradford, UK, in 2010, and M.Eng. and B.Eng degrees in Electronic Engineering from Huazhong University of Science and Technology, Wuhan, China, in 2006 and 2004, respectively.
The University of Exeter and Tsinghua University have launched a jointly-awarded PhD degree programme in climate and environmental sciences which supports six students to be co-supervised between Tsinghua's Department of Earth System Science and Colleges at Exeter that conduct research on earth systems and environmental sciences. Read more.
This programme is research-led. More than 94% of our Computer Science research outputs are internationally excellent. Students have the opportunity to carry out work on the University's High Performance Computing environment which represents a £3m investment by the University, designed to serve the advanced computing requirements.
The University of Exeter and Tsinghua University have launched a jointly-awarded PhD degree programme in climate and environmental sciences which supports six students to be co-supervised between Tsinghua's Department of Earth System Science and Colleges at Exeter that conduct research on earth systems and environmental sciences. Read more.
We have 5 University of Exeter Computer Science PhD Projects, Programmes & Scholarships. University of Exeter. A Russell Group university with world-class research and high student satisfaction. Find out more. Show more Show all . More Details . AI for the Environment: Collaborative Exeter-Turing PhD Studentship.
The University of British Columbia. Activities between the University of British Columbia and Exeter include a joint research symposium focused on Community, Culture, Creativity, and Wellbeing held at Exeter in May 2018 and a faculty-led, co-funded initiatives in Sport, Exercise and Health Sciences, Climate Change and Digital Humanities.
The University of Exeter and Tsinghua University have launched a jointly-awarded PhD degree programme in climate and environmental sciences which supports six students to be co-supervised between Tsinghua's Department of Earth System Science and Colleges at Exeter that conduct research on earth systems and environmental sciences. Read more.
PhD in Computer Science at the prestigious University of Exeter is a prestigious degree that offers in-depth learning in Computer Science. Being a renowned university, University of Exeter receives enough funds to ensure the best education facilities for its students across all programs. This doctorate program offered full-time primarily focuses on the practical implementation of fresh ideas ...
Professor Neil Vaughan is Associate Professor of Data Science and AI at University of Exeter. He recently completed Royal Academy of Engineering Research Fellowship. ... Professor Neil Vaughan is available to supervise more PhD or post-doc research projects. Current PhD funding opportunities: EPSRC DTP, MRC, China Scholarship Council (CSC) and ...
The University of Exeter and Tsinghua University have launched a jointly-awarded PhD degree programme in climate and environmental sciences which supports six students to be co-supervised between Tsinghua's Department of Earth System Science and Colleges at Exeter that conduct research on earth systems and environmental sciences. Read more.
As an Exeter Computer Science and IT graduate you may find yourself working with business IT systems, the web, mobile communications, games technology, or in the management and development of the safety-critical systems that control planes, trains and power stations.
In the PhD in Computer Science program at Columbia Engineering, you'll find a vibrant, collaborative community of research with broad interests including natural language processing, security and privacy, graphics and user interfaces, computational biology, computer vision, robotics, machine learning, and artificial intelligence. ...
Course Summary. Overview. Our main areas of Computer Science research include Artificial Intelligence, Computer Vision, Cyber Security, Data and Network Science, Evolutionary Computing and Optimisation, High Performance Computing and Networking, and Machine Learning. The. as well as a list of our current postgraduate researchers.
The University of Exeter and Tsinghua University have launched a jointly-awarded PhD degree programme in climate and environmental sciences which supports six students to be co-supervised between Tsinghua's Department of Earth System Science and Colleges at Exeter that conduct research on earth systems and environmental sciences. Read more.