Postgraduate research study
We offer an enhanced PhD training programme to address the world’s demand for individuals with scientific expertise in AI and machine perception, as well as professional and cross-disciplinary skills required to develop and deploy future AI intelligent sensing technologies for modern life.
We acknowledge, understand and embrace diversity.
PhD at CVSSP
Postgraduate research at CVSSP
There is no better time to join us!
Our vision is to engage PhD, post-doctoral and industry researchers in AI and machine perception across the UK in high-quality training and networking with industry to ensure that the UK leads the future development of this field.
Our training programme enables PhD students to gain the research skills needed to obtain an internationally-recognised PhD including:
- Multi-disciplinary knowledge of the fundamental science and practical application of multi-sensory machine perception
- Experience of applying their research in industry and the public sector to real-world challenges and data; understanding of responsible research and innovation
- Transferable skills such as entrepreneurship and communication, including engineering competence skills suitable for professional registration (EurIng, CEng)
- An open science approach to research, through open publication of research papers, data and software
- Entrepreneurial and innovation-oriented attitude through exposure to SME and spin-off companies in our network.
CVSSP is a destination of choice for postgraduate talent and it is part of the Department of Electrical and Electronic Engineering which is ranked 6th in the UK for electrical and electronic engineering in the Guardian University Guide 2021.
What we offer
- Cohort-based research training with training weeks on transferable skills, science schools on research topics and facilitated sandpits for agile cross-disciplinary team solution to industry challenges.
- Individual research training to develop in- depth research experience and specific skills tailored for the individual and their personal career development plan
- Opportunities for engagement with non-academic industry and user partners throughout the research programme incl. project design, training in industrial practice and industrial secondments. The learning objective is to ensure that all PhD researchers gain a cross-disciplinary understanding of both the fundamental science and real-world application of multi-sensory machine perception.
We have a thriving community of over 90 postgraduate research students conducting research across a broad range of areas. Our research cohorts are internationally diverse with students from all over the world, studying on a full or part-time basis. You can view our research or contact the relevant academic with an informal enquiry.
Postgraduate research courses start in October, January, April and July and run for 48 months full time. Offers may be conditional of applicants achieving the minimum requirements for IELTS and having satisfactory academic qualifications. Contact CVSSP Admin if you have a general enquiry.
The Centre for Vision Speech and Signal Processing (CVSSP) specifically, is a great place to undertake a PhD in machine learning or machine perception, as it has a very diverse range of research groups all with their own area of expertise. And we also have great Christmas parties.Lewis Bridgeman. Deep Learning for Free-viewpoint Video in Sports and Immersive VR Experiences
Transform future media experiences
Six fully-funded PhD studentships including fees and stipend available for outstanding candidates to join the BBC partnership 'AI4ME' at the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey.
PhD studentship opportunities at CVSSP
Centre for Doctoral Training in Audio-Visual Machine Perception (CDT-AVMP)
Our Centre for Doctoral Training offers an enhanced doctoral training programme to address industry demand for individuals with cross-disciplinary scientific expertise in AI and machine perception, working in teams with integrated professional skills in software, project management, innovation and understanding of ethics and regulation of AI and data science research.
We are looking for committed and enthusiastic applicants who want to be part of a rich and stimulating research environment where you will be able to contribute to advancing the state-of-the-art through publications and by participating in leading international forums. Secondments with industry and international groups are encouraged during the programme to broaden your experience and to ensure our research is kept relevant to real-world problems.
Funding is available for up to six PhD studentship opportunities in the following research areas:
- Audio-visual AI
- Computer vision
- Machine learning
- Data science for media
- Computational photography, 3D and 4D video
- Spatial audio and machine audition
- Robot vision and autonomous systems
- Biometrics and surveillance
- Medical imaging analysis and healthcare technology
- Multimedia communication systems
- Digital doubles and free-viewpoint video
- Vision and audio for TV, film, games, virtual and augmented reality.
For further information please contact Nan Bennett.
The aim of this project is to develop new methods for automatic labelling of sound environments and events in broadcast audio, assisting production staff to find and search through content, and helping the general public access archive content. The project will undertake a combination of interviews and user profiling, analysis of audio search datasets, and categorisation by audio experts to determine the most useful terminology for production staff and the general public as user groups. The project will develop a taxonomy of labels, and examine the similarities and differences between each group. The project will also investigate the application of a labelled library in a production environment, examining workflows with common broadcast tools, then integrating and evaluating prototype systems. The project will also investigate methods for automatic subtitling of non-speech sounds, such as end-to-end encoder-decoder models with alignment, to directly map the acoustic signal to text sequences. Working with BBC R&D, the student will develop software tools to demonstrate the results, especially for broadcasting and the management of audiovisual archive data, and benchmark the results against human-assigned tags and descriptions of audio content. Using archive data provided by BBC R&D, the student will engage with audio production and research experts through Expert Panels, and potential end users through Focus Groups.
The project will be supervised by Prof Mark Plumbley, as part of the EPSRC Fellowship on AI for Sound in the Centre for Vision, Speech and Signal Processing. As part of this PhD, you will have the opportunity for close day-to-day collaboration with the BBC as a member of the R&D Audio Team. You will have inside access to meetings, data, tools and technology, and be able to work alongside a wide range of BBC staff.
We acknowledge, understand and embrace diversity.
01 August 2021
The aim of this PhD project is to investigate information theoretic methods for analysis of sounds. The Information Bottleneck (IB) method has emerged as an interesting approach to investigate learning in deep learning networks and autoencoders. As well as traditional Shannon entropy, the Information Bottleneck method also applies to Renyi and other entropies. Fast and accurate estimation of information is still an active area of research. This project will investigate information-theoretic approaches to analyse sound sequences, both for supervised learning methods such convolutive and recurrent networks, and unsupervised methods such as variational autoencoders. The project will also investigate direct information loss estimators, and new information-theoretic processing structures for sound processing, for example involving both feed-forward and feedback connections inspired by transfer information in biological neural networks.
This project will focus on real time, complex urban semantic segmentation where the temporal evolution of the scene is used to increase segmentation accuracy. Although the dynamics of the scene can be used in segmentation they can also be used to predict the evolution of the scene and the future actions of other road users. Importantly this would allow an AI vision system to process an incoming video stream in real time, break that scene into its constituent components and answer questions such as “what do we expect the scene to look like in five seconds?”.
The PhD will be supervised by Prof. Richard Bowden based at the Centre for Vision Speech and Signal Processing (CVSSP). We acknowledge, understand and embrace diversity.
This research will investigate the transformation of monocular audio and visual video into a spatially localised object-based audio-visual representation.
Self-supervised and weakly supervised deep learning will be investigated for the transformation of general scenes into semantically labelled and localised objects. This will build on recent advances in deep-learning based monocular reconstruction of general dynamic scenes and objects with known semantic labels, such as people. Multi-modal information sources including audio and text subtitles will be employed to support weakly supervised learning for semantic labelling and object-based reconstruction.
The goal of this research is to generalise to unconstrained video sequences of complex real-world scenes with multiple interacting people. Research will investigate approaches for the transfer of multi-modal or additional information to support the object-based scene reconstruction and evaluate the relative importance of different information sources.
The approach should be able to achieve plausible reconstruction of unknown or unmodelled object classes, together with complete reconstruction for modelled object classes. Learning on in-the-wild and BBC archive datasets will be investigated to support the generalisation to complex scenes. Specific use-cases such as sports and programme recommendation will also be investigated for evaluation in constrained contexts. The approach will be evaluated on both live and legacy content.
15 July 2021
The Centre for Vision Speech and Signal Processing (CVSSP) seeks applications from high-quality students for a fully funded studentship, on Active Machine Perception. Active machine perception is essential to develop intelligent systems capable of safely interacting with humans and their surroundings. The aim of this PhD is to significantly advance machine perception to enable machines to function autonomously in our day-to-day environment. The student will be based at CVSSP in the UK with the opportunity to collaborate with Agency for Science, Technology and Research (A*STAR), Singapore during the project.
The project will explore human-like scene understanding on dynamic scenes from single-view/multiple-view videos exploiting recent advances in Artificial intelligence (AI). This research will solve an open challenging problem and enable machines to function autonomously in real-world social scenes, creating an impact in various fields such as autonomous driving and robotics.
The PhD will be jointly supervised by Dr Armin Mustafa and Prof. Adrian Hilton based at the Centre for Vision Speech and Signal Processing (CVSSP). There will be collaboration opportunities with A*STAR, Singapore. We acknowledge, understand and embrace diversity.
05 July 2021
We seek applications from enthusiastic, self-motivated students for up to three funded PhD studentships within the newly awarded DECaDE: Centre for the Decentralized Digital Economy, led by the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey and in partnership with the University of Edinburgh, the Digital Catapult, and over 30 industry and public sector partners.
The student will be based at CVSSP in the UK with opportunity for internship and travel to DECaDE centre partners during the course of the PhD. Funded doctoral research projects will investigate the potential for decentralized platforms to transform the Digital Economy, particularly looking at Artificial Intelligence (AI) technologies application in distributed systems and data. Research topics include federated machine learning (FML), computer vision and visual analytics, machine learning over distributed sensor networks, AI for distributed media, and the intersection AI and cyber security. We acknowledge, understand and embrace diversity.
The Centre for Vision Speech and Signal Processing (CVSSP) at The University of Surrey and the metrology for Medical Physics Group (MEMPHYS) at The National Physical Laboratory are pleased to offer a PhD opportunity in the field of medical imaging. The studentship is part of a unique partnership with Alliance Medical Limited – Europe’s largest independent provider of medical imaging.
The project will support standardisation for imaging in large scale studies using a combination of artificial intelligence (AI), big data, biomarker and measurement science approaches. The purpose of the studentship is to bring new AI methods to bear on understanding medical images for cancer treatment with the potential to predict treatment outcomes using these new methods. The studentship is anticipated to work on methods and applications for CT (computed tomography) scanning and/or PET (positron emission tomography).
We acknowledge, understand and embrace diversity.
The Centre for Vision Speech and Signal Processing (CVSSP) at The University of Surrey are pleased to offer a PhD opportunity in the field of medical imaging. The studentship is part of a unique partnership with: the metrology for Medical Physics Group (MEMPHYS) at The National Physical Laboratory; The Royal Surrey NHS Foundation Trust and, Alliance Medical Limited – Europe’s largest independent provider of medical imaging.
The project will be part of our work to standardise medical imaging and its analysis, to develop AI methods for medical imaging analysis and interpretation and to build a unique methodology for big data studies in healthcare.
For enquiries contact Prof Phil Evans indicating your areas of interest and including your CV with qualification details (copies of transcripts and certificates). Shortlisted applicants will be contacted directly to arrange a suitable time for an interview. We acknowledge, understand and embrace diversity.
This project is under the supervision of Professor Richard Bowden who runs the cognitive vision lab within CVSSP. He has a track record of work into various areas of computer vision which include visual tracking and the location, tracking, and understanding of humans. You will be joining a thriving research group with ongoing projects into perception for autonomous vehicles, sign language recognition/production and robotics and AI.
Depending upon the skillset of the applicant, this studentship will research and develop new deep learning approaches to either visual tracking which can be applied to video object tracking, human tracking or the application of such technology to the recognition and production of human sign and gesture.
For enquiries contact Prof Richard Bowden indicating your areas of interest and including your CV with qualification details (copies of transcripts and certificates). Shortlisted applicants will be contacted directly to arrange a suitable time for an interview. We acknowledge, understand and embrace diversity.
Our research has pioneered new technologies for the benefit of society and the economy, with applications spanning healthcare, security, entertainment, robotics, autonomous vehicles, communication and audio-visual data analysis.
CVSSP research facilities
We have unique facilities to support audio-visual machine perception research, including: a multiple camera audio-visual recording studio with 16 Ultra HD cameras, video rate capture and processing, a 64 channel sound sphere, and robot vision platforms including a new autonomous car testbed.
PhD study at University of Surrey
Find out more about PhD study at the University of Surrey from the people at the heart of our doctoral research community – our incredible students and academics.
PhD programme details
Please contact Nan Bennett once you have applied for a PhD position or successfully submitted a PhD online application form.