We use cookies to improve your experience of our website. Privacy Policy

Skip to main content

Institute of Applied Data Science


Centres for Doctoral Training

Queen Mary University of London hosts a number of Centres for Doctoral Training (CDT) programmes linked across key themes of Artificial Intelligence and Data Science research.

UKRI funded Centres for Doctoral Training bring together diverse areas of expertise to train scientists with the skills, knowledge and confidence to tackle today’s evolving issues, and future challenges. They also provide a supportive and exciting environment for students, create new working cultures, build relationships between teams in universities and forge lasting links with industry.

CDT in Artificial Intelligence and Music

UKRI Centre for Doctoral Training in Artificial Intelligence and Music [EP/S022694/1]

PIs: Professor Simon Dixon, Professor Mark Sandler, Professor Nicholas Bryan-Kinns

Active: July 2019 to December 2027

Project Partners include: Abbey Road Studios, Channel Four Television Corporation, Spotify, Steinberg Media Technologies GmbH, Universal Music, Solid State Logic.

Summary: The UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM) will train a new generation of researchers who combine state-of-the-art ability in artificial intelligence (AI), machine learning and signal processing with cross-disciplinary sensibility to deliver ground-breaking original research and impact within the UK Creative Industries (CI) and cultural sector.

The core area of this CDT is Music Information Research (MIR, also known as Music Informatics), which involves the use of intelligent information processing methodologies to understand and model music, and to develop products and services for creation, distribution, interaction and experience of music and music-related information.

CDT in Media and Arts Technology

EPSRC and AHRC Centre for Doctoral Training in Media and Arts Technology [EP/L01632X/1]

PIs: Professor Nick Bryan-Kinns, Professor Mark Sandler

Active: May 2014 to October 2022

Project Partners include: Barbican Centre for Arts and Conferences, Department for International Trade, BBC, Georgia Institute of Technology, IBM, Nesta, BT, Procter & Gamble, Victoria and Albert Museum.

Summary: The CDT in Media and Arts Technology will train PhD students to become skilled researchers and practitioners at the intersection of science, technology, digital media and the arts. The proposed CDT builds on the outstanding success of Queen Mary's current Media and Arts Technology (MAT) programme, introducing new training elements in Design, Innovation and Materials and expanded industrial and international partnerships. It addresses all 3 of EPSRCS's Digital Economy themes, particularly Digitally Connected Citizens and many Information and Communication Technologies (ICT) themes, especially Next Generation Interaction Technologies, Data to Knowledge and ICT for Manufacturing; Digital Healthcare.

The MAT CDT has an established network of over 40 external partners including: large companies (BBC, IBM, Orange, Sony and Procter & Gamble) health organisations (Royal Hospital of Neurodisability) and Tech City SMEs (Cinimod, Lean Mean Fighting Machine, Ustwo, Playgen, United Visual Artists, Hide&Seek, Troika), cultural institutions (Barbican, Science Museum and V&A), and governmental bodies (UKTI, TCIO, DSTL and London & Partners). Many partners host students' Advanced Placement Project, provide data sets and technical resources, supervision and mentoring, and exposure to a wide range of markets and audiences. The CDT acts as a focus bringing together otherwise disparate external bodies who discover shared interests and values.

The CDT led to a major expansion in Digital Media research and teaching at Queen Mary. It inspired the creation of both a MSc in Media and Arts Technology and a BSc(Eng) in Multimedia and Arts Technology. The University invested around £3M in 200m2 of facilities for the MAT CDT, including Media and Arts Technology Studios, CDT hub (work/meeting space), 'maker' workshops, and a multimedia IT suite for audio/video editing.

CDT in Intelligent Games and Game Intelligence

EPSRC Centre for Doctoral Training in Intelligent Games and Game Intelligence [EP/S022325/1]

PIs: Professor Paul Cairns (University of York) and Professor Peter Cowling (Queen Mary University of London)

Period: October 2019 – March 2028

Project Partners include: Sony Computer Entertainment Europe, Microsoft Research Ltd, Nokia Bell Labs, British Games Institute, BBC, Electronic Arts, Ubisoft Massive Entertainment, National Science and Media Museum.

Summary: Digital games have extraordinary economic, social and cultural impact. The industry is one of the fastest-growing in the world, larger than film or music, with revenues expected to increase from $138 billion in 2018 to $180 billion by 2021. The UK games industry is a global leader - UK game sales are valued at £4.3bn with 12,000 people directly employed. The games industry is innovative and hungry for innovation - recent research breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML) have arisen through games research undertaken at Google DeepMind in the UK. Rolls Royce makes better jet engines using 3D technology pioneered in games. Games are leading the "data and AI revolution" of HM Government's 2017 Industrial Strategy.

The EPSRC Centre for Doctoral Training in Intelligent Games and Game Intelligence (IGGI) first received funding in 2014, and has since been a huge success: raising the level of research innovation in games, with the highest-possible ratings in our EPSRC mid-term review. The next phase of IGGI will inject 60+ PhD-qualified research leaders and state of the art research advances into the UK games industry. IGGI will massively advance these research themes, and train 60+ PhD students to be future research leaders. To accomplish this, our updated training programme and 60+ research supervisors will provide students with rigorous training and hands-on experience in AI, programming, game design, research methods, and data science, with end user and industry engagement from day one. Recruiting and empowering a diverse student cohort to promote equality, diversity, and inclusion through games, IGGI will drive positive culture change in industry and academia.

CDT in Data-Centric Engineering

UKRI Centre for Doctoral Training in Data-Centric Engineering

PI: Professor Eram Rizvi

Period: October 2020 – March 2026

Project Partners include: IBM, BT, BBC, Qinetiq, TWI Ltd., Baxter Healthcare, Tower Hamlets London Borough Council.

Summary: The Centre for Doctoral Training (CDT) in Data-Centric Engineering, forms part of the UK Research and Innovation's (UKRI) Doctoral Mobility Pilot, which aims to increase mobility across the industry and academic sectors and enhance research skills of individuals from industry and other non-academic backgrounds. The CDT will increase the number of scientists and engineers with data science and engineering research skills in the UK economy and widen access routes and increase the diversity of our doctoral scholars. Doctoral scholars will be trained to work seamlessly across academic and industrial sectors, and qualify with professional doctorates. The CDT in Data-Centric Engineering offers a professional doctorate qualification focused on workplace-based applied research, co-created with industry partners.  Doctoral scholars will benefit from a high-quality bespoke training programme with access to world-class researchers and facilities, and the direct workplace based application of research methods in the live industrial settings of our partners including IBM, BT and the BBC.

Mini-CDT in Data-informed Audience-centric Media Engineering

Queen Mary funded mini Centre for Doctoral Training in Data-informed Audience-centric Media Engineering

PIs: Professor Mark Sandler, Dr Gareth Tyson, Dr Charalampos Saitis

Period: 2020-2024

Project Partners include: BBC, SFI Research Centre.

Summary: DAME is a leading PhD programme funded by Queen Mary University of London (QMUL) and the BBC to produce world-class researchers in technologies that support future products and services in online media, capitalising on techniques of Data Science, Artificial Intelligence, Machine Learning and novel mathematics and statistical techniques. This contributes to the emergent vision of a data-driven economy to bring new experiences to the Audience of the Future, where emergent trends like Virtual and Augmented Reality (VR/AR), second screen and immersion are coming to the fore. It will operate in close collaboration with the BBC, particularly their Data Science Research activity and with the SFI Research Centre, Insight. Based at Queen Mary’s School of Electronic Engineering and Computer Science and the Institute of Applied Data Science, DAME students undertake a four-year PhD focused on developing cutting-edge research in collaboration with the BBC, our key industry partner.

Mini-CDT in AI-based Cardiac Image Computing (AICIC)

Queen Mary funded mini Centre for Doctoral Training in AI-based Cardiac Image Computing (AICIC)

PIs: Professor Greg Slabaugh, Dr Qianni Zhang

Period: 2021-2025

Project Partners include: NVidia, Circle Cardiovascular Imaging, Conavi Medical, King’s College London Centre for Value Based Healthcare

Summary: The AI-based Cardiac Image Computing mini-CDT unites Queen Mary researchers working in AI-based cardiac image computing across four different Schools / Institutes (School of Electronic Engineering and Computer Science, School of Mathematical Sciences, Digital Environment Research Institute, the William Harvey Research Institute) and drives a collective vision that AI will have a transformative effect in cardiovascular disease understanding, diagnosis, risk stratification and treatment.  Aligned to this vision, the mini-CDT will train three PhD students in the area of AI-based cardiac image computing.  The research will develop new AI methods to characterise cardiovascular disease along with new image analysis tools to diagnose patients.  The mini-CDT is timely because cardiovascular disease is the leading cause of death globally and recent advances in deep learning have resulted in major breakthroughs image computing.  The project is in collaboration with external partners in industry (NVIDIA, Conavi Medical Inc, Circle Cardiovascular Imaging) and academia (King’s College London) and each mini-CDT student is expected to spend a minimum of three months working in industry through an internship.  This mini-CDT brings together Queen Mary academics across technical and clinical disciplines to train next generation researchers who understand both the technology and clinical challenges, resulting in clinically-relevant AI skills for expected real impact in healthcare.

Mini-CDT in Data Analytics of Large Coupled Structures

Queen Mary funded mini Centre for Doctoral Training in Data Analytics of Large Coupled Structures (DatACoSt)

PIs: Dr Kathrin Glau

Period: 2021-2025

Project Partners include: Bank of America Merrill Lynch, HSBC, IBM, Bloomberg, Yahoo Research, Telefonica.

Summary:  The Mini-CDT in Data Analytics of Large Coupled Structures will set up three specific PhD projects, which link the mathematics of data analytics with challenges in Medicine, Engineering, Computer Science, Finance and Economics. Each topic will have a close link to a partner department in the university: “Emulating sounds with deep learning” with input from EECS, “Reliable and efficient analysis of financial data for risk management” with input from SEF, and “Modelling stochastic processes in human population genetics” with input from SMD. As an overarching principle these topics are addressing key issues in Data Analysis of complex coupled systems. A common theme of the PhD projects is given by the specific methodology used to address the individual challenges. This methodology, summarised under the term “Dynamical Mode Decomposition”, has proven its success in applications such as climate research, forecasting, or risk assessment, has its foundation in Functional Analysis, and therefore constitutes a crucial bridge between rigorous mathematics and applications in science and engineering and beyond. As a key technology that has emerged in recent years this approach impacts on fields as diverse as Social Sciences, Medicine, Biology and Chemistry, Engineering, Computer Science and Physics.

Mini-CDT in Amorphous Nanocatalysts for CO2 Reduction Reaction 

Queen Mary funded mini Centre for Doctoral Training in Amorphous Nanocatalysts for CO2 Reduction Reaction (AmorCO2RR)

PIs: Dr Devis Di Tommaso, Dr Cristina Giordano, Dr Ana Sobrido

Co-PIs: Dr Rachel Crespo-Otero, Dr Joe Briscoe, Dr Greg Chass, Dr Stellios Arseniyadis, Professor Greg Slabaugh

Period: 2021-2025

Project Partners include: Johnson Matthey, RISE, National Physics Laboratory, UCL, National University of Singapore

Summary: AmorCO2RR is an academic-industrial collaboration between eight leading academics from School of Biological and Chemical Sciences, School of Engineering and Materials Science and School of Electronic Engineering and Computer Science with the objective of developing a novel class of amorphous catalysts for the electrochemical transformation of CO2 into value-added chemicals, such as easily transformable carbon monoxide and hydrocarbons. The proposal is timely because the rising level of CO2 in Earth’s atmosphere is undoubtedly the biggest environmental challenge that our society has to face. The  AmorCO2RR approach is trans-disciplinary. It is a collaboration between experts in nanomaterials, computational and experimental catalysis, scientific software development, neutron scattering, and machine learning, to create doctoral skills necessary to achieve the UK targets of reducing emissions by recycling CO2 into chemicals, fuels and materials. Based at Queen Mary’s Schools of Biological and Chemical Sciences, Engineering, and Computer Science, AmorCO2RR students undertake a four-year PhD focused on developing cutting-edge research in collaboration with the Johnson Matthey, UCL and the National University of Singapore, our key industrial (Johnson Matthey, RISE), governmental (NPL) and academic (UCL, Singapore) partners.