Information Systems and Computing Integrated PhD

Program Overview

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Research profile

Our focus is interdisciplinary research that includes researchers with a range of backgrounds including computer science, engineering, mathematics, and psychology. We carry out rigorous world-leading applied research in a range of related topics including software engineering, intelligent data analysis, human-computer interaction, information systems, and systems biology. Much of our research relates to two main domains: healthcare/biomedical informatics and digital economy/business.

Find out about the exciting research we do in Computing. Browse profiles of our experts, discover the research groups and their inspirational research activities you too could be part of. We’ve also made available extensive reading materials published by our academics and PhD students.

Learn more about research in this area.

You will benefit from this integrated PhD programme immensely if you want to:

receive a more much guided and hands-on supervision of your learning and research process, especially if you come from more traditional teaching cultures

increase your chances for timely completion of your PhD programme in comparison to students taking traditional route PhD, cutting down the expenses associated with prolonged study

access to tailored, highly specialist research training not available as part of the support provided to traditional route PhD students

maximise your chances for a successful research analysis by applying practical assignments and training which are part of the integrated PhD directly to the research you do for your thesis

receive an official Postgraduate Diploma in Research in addition to your PhD award to certify the completion of skills training which may be required by employers in some countries if you wish to pursue an academic career

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Browse the work of subject-relevant research groups

Intelligent Digital Economy and Society

Human Computer Interaction

Brunel Software Engineering Lab

AI Social and Digital Innovation

Intelligent Data Analysis

Institute of Digital Futures

Modelling and Simulation

Computer Science for Social Good

Computational Biology

Interactive Multimedia Systems

Digital Economy

Smart Technology Advancements in Health and Rehabilitation

You can explore our campus and facilities for yourself by taking our virtual tour.

Program Outline

Research journey

The Brunel Integrated PhD combines PhD research with a programme of structured research, professional and subject training. The programme typically takes 4 years (compared to 3 years for a non-integrated PhD programme). On successful completion, you will be awarded a PhD with an Integrated Postgraduate Diploma in Research in your chosen subject specialisation.

The programme involves demonstrating through original research or other advanced scholarship the creation and interpretation of new knowledge, a systematic acquisition and understanding of a substantial body of knowledge at the forefront of an academic discipline or professional practice, the ability to conceptualise, design and implement a project for the general of new knowledge, applications or understanding at the forefront of the discipline.

The programme of taught modules runs in parallel to your research work during the first three years of study, with the fourth year providing time for you to focus on writing up your PhD thesis. The taught modules cover research and professional skills as well as providing discipline-specific content. The Brunel Integrated PhD aims to support an individual’s development as a research professional. It aims to produce researchers who are well prepared to embark on careers as academics or professional researchers. As well as the skills to conduct and disseminate high-quality academic research, researchers will develop a range of broader (‘transferable’) skills to help ensure that their work has an impact in the wider world.

Find out more here.

This course can be studied 4 years full-time, starting in January. Or this course can be studied 4 years full-time, starting in October.

Find out about what progress might look like at each stage of study here: Research degree progress structure.

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Careers and your future

You will receive tailored careers support during your PhD and for up to three years after you complete your research at Brunel. We encourage you to actively engage in career planning and managing your personal development right from the start of your research, even (or perhaps especially) if you don't yet have a career path in mind. Our careers provision includes online information and advice, one-to-one consultations and a range of events and workshops. The Professional Development Centre runs a varied programme of careers events throughout the academic year. These include industry insight sessions, recruitment fairs, employer pop-ups and skills workshops.

In addition, where available, you may be able to undertake some paid work as we recognise that teaching and learning support duties represent an important professional and career development opportunity.

Find out more.

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Find a supervisor

Our researchers create knowledge and advance understanding, and equip versatile doctoral researchers with the confidence to apply what they have learnt for the benefit of society. Find out more about working with the Supervisory Team.

You are welcome to approach your potential supervisor directly to discuss your research interests. Search for expert supervisors for your chosen field of research.

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PhD topics

While we welcome applications from student with a clear direction for their research, we are providing you with some ideas for your chosen field of research:

A Machine Learning Approach for Migrating to Microservices, supervised by Nour Ali

Additive manufacturing and sustainability, supervised by Eujin Pei

Advanced planar MIMO sparse imaging for target detection, supervised by Hongying Meng and Shaoqing Hu

Automatic computational fluid-dynamics, supervised by James Tyacke

Autonomous robots for non-disruptive inspection of utility and sewage systems, supervised by Md Nazmul Huda

Brain wave analysis and modelling with graph signal processing, supervised by Nikolaos Boulgouris

Building Information Model Development Using Generative Adversarial Networks, supervised by Michael Rustell and Tatiana Kalganova

Can AI based robot car win the race, supervised by Dong Zhang

CFD modelling of plasma flow control, supervised by James Tyacke

Cultivating virtual embodiment using non-invasive brain stimulation, supervised by Monica Pereira and Nadine Aburumman

Deep Learning for Medical Imaging, supervised by Yongmin Li

Deep learning-based autonomous diagnosis of gastrointestinal tract cancers, supervised by Md Nazmul Huda

Design and Development of a Capsule Robot for Medical Applications, supervised by Md Nazmul Huda

Developing computational models to understand the evolution of bidirectional catalysts in biology, supervised by Sarath Dantu

Development of personalised services and applications for healthcare, supervised by Fotios Spyridonis, George Ghinea, Zidong Wang and Xiaohui Liu

Development of resilient hospitals through enhanced built environment design and research, supervised by Kangkang Tang

Digital Stone: Robotic Construction of a Masonry Arch Bridge, supervised by Michael Rustell and Tatiana Kalganova

Disruptive Digital Experiences, supervised by Harry Agius and Damon Daylamani-Zad

Energy and CO2 Awareness during Software Design and Development, supervised by Nour Ali

Explaining model decisions through dialogue, supervised by Isabel Sassoon

Exploring the potential of serious games to enhance user engagement with real-world applications, supervised by Fotios Spyridonis and Damon Daylamani-Zad

Intelligent, Interpretable and Adaptive Design of Steel Structures using Deep Learning and NLP, supervised by Michael Rustell and Tatiana Kalganova

Large Language Models (LLM) for Automated Finite Element Analysis, supervised by Michael Rustell and Tatiana Kalganova

Machine learning approaches in health data science for risk prediction of cardiovascular diseases, supervised by Raha Pazoki

Machine learning for natural language modelling and processing, supervised by Nikolaos Boulgouris

Machine learning for sustainable transportation systems, supervised by Muhammad Shafique

Natural Language Processing for Business Intelligence, supervised by Yongmin Li

Real-time Visual and Haptic Feedback of Grasping Movements in Virtual Reality, supervised by Nadine Aburumman

Swarm of multiple co-operative and autonomous low-cost robots for search and rescue, supervised by Md Nazmul Huda

Use of Large Language Models (LLM) as a Structural Engineering Design Assistant, supervised by Michael Rustell and Tatiana Kalganova

User experience in Extended Reality environments and applications, supervised by Fotios Spyridonis and Damon Daylamani-Zad

Using deep learning for weed detection, supervised by Tatiana Kalganova

Using Machine Learning to Simulate Macroscopic phenomena for Fluid Dynamics, supervised by Nadine Aburumman