Develop your own self-funded PhD proposal
If you have your own research idea, we can help you to develop it. To begin this process you will need to find a PhD Supervisor from one of our research groups, whose research interests align with your own.
Apply for a funded PhD position
We offer a range of PhDs funded by different sources, such as research councils, industries or charities.
To apply for a funded PhD please read the advertised project information carefully as requirements will vary between funders. The project information will include details of funding eligibility, application deadline dates and links to application forms. Only applicants who have a relevant background and meet the funding criteria can be considered.
1. Current PhD Opportunities
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Details
Start Date: September 2024, with the flexibility to start later if necessary
Deadline for applications: Open-ended: please apply as soon as possible
Interview Date: To be confirmed
Academic Requirements: First-class or 2.1 (Hons) degree, or Master's degree (or equivalent) in an appropriate subject
Description
A fully funded PhD studentship open to UK applicants, including fees and stipend, are available for candidates to join the BBC partnership 'AI4ME' in the School of Computing & Communications at Lancaster University.
AI4ME is an exciting five-year EPSRC & BBC-funded Prosperity Partnership that is addressing the key challenges involved in creating and delivering personalised content at scale. AI4ME brings together the BBC, Lancaster University and the University of Surrey to design entirely new types of media experiences that can adapt to individual preferences, accessibility requirements, devices, and location. This is a unique opportunity for students to join a major new research partnership and take advantage of this vibrant collaborative research environment.
AI4ME builds upon the rapid changes underway within the industry in how media experiences are produced and delivered, from television and films to video games. In television and broadcast media, the prevalence of Internet-based delivery supports the independent transport of different parts of a stream – including audio, video, and additional media experience components – to be composed together at the point of playback. This paradigm is enabling new forms of hyper-personalised and immersive storytelling and represents both opportunities and new challenges in the network delivery of these experiences.
We are looking for two PhD candidates to be part of our exciting journey in building the world’s first hyper-adaptive end-to-end delivery infrastructure to deliver personalised object-based media on an unprecedented scale. This will require research in computer networking and distributed systems.
The following represent possible areas that could form the basis of your PhD:
- Novel Self-Adaptive Distributed Systems Paradigms: new approaches to building distributed systems, in which self-adaptive behaviours are at the core of the system’s capability.
- Distributed Reinforcement Learning: developing new techniques which scale well for state-of-the-art real-world stream processing systems.
- Orchestration Methods, Models, and Languages: how orchestration models or languages can be used within a highly adaptive deployment infrastructure to provide services that meet specific delivery criteria.
About You
You will have a 1st or 2:1 (Hons) degree in Computer Science (or related field), or a Master's (or equivalent) in a relevant engineering or scientific discipline or equivalent specialist experience. You need to have a genuine interest in computer networking or distributed systems and ideally be able to demonstrate strong computer programming skills. Evidence of research skills, for example, through a significant Bachelor's / Master's project involving experimental research, appropriate use of the literature and a formal dissertation-style report will be considered a plus.
Funding
These PhD studentships are open to UK students and cover university tuition fees for 4 years and a tax-free maintenance grant (stipend) of £19,237k per annum. The studentships also include funding to support travel costs to present research at national and international conferences.
Application Process and Next Steps
General enquiries are welcomed by Professor Nicholas Race by email (networkedsystems@lancaster.ac.uk)
Otherwise, you may apply directly: Applying for postgraduate study mentioning the “AI4ME PhD studentship”.
Details
Start Date: September 2024, with the flexibility to start later if necessary
Deadline for applications: Open-ended: please apply as soon as possible
Interview Date: To be confirmed
Academic Requirements: First-class or 2.1 (Hons) degree, or Master's degree (or equivalent) in an appropriate subject
Description
A fully funded PhD studentship open to UK applicants, including fees and stipend, are available for candidates to join the BBC partnership 'AI4ME' in the School of Computing & Communications at Lancaster University.
AI4ME is an exciting five-year EPSRC & BBC-funded Prosperity Partnership that is addressing the key challenges involved in creating and delivering personalised content at scale. AI4ME brings together the BBC, Lancaster University and the University of Surrey to design entirely new types of media experiences that can adapt to individual preferences, accessibility requirements, devices, and location. This is a unique opportunity for students to join a major new research partnership and take advantage of this vibrant collaborative research environment.
AI4ME builds upon the rapid changes underway within the industry in how media experiences are produced and delivered, from television and films to video games. In television and broadcast media, the prevalence of Internet-based delivery supports the independent transport of different parts of a stream – including audio, video, and additional media experience components – to be composed together at the point of playback. This paradigm is enabling new forms of hyper-personalised and immersive storytelling and represents both opportunities and new challenges in the network delivery of these experiences.
Quality of Experience (QoE) is a key component of multimedia delivery. It measures the perceived quality of multimedia experiences, allowing for better decision-making in the delivery chain. Object-Based Media allows for a new kind of multimedia experiences which offer greater levels of interaction and personalisation. This presents a unique opportunity to improve the QoE in the multimedia world while exploring areas such as: Artificial Intelligence, Machine Learning, Computer Vision, Computer Networks and Cloud Gaming. We are looking for a PhD candidate interested in one of these areas as well as QoE.
Potential PhD themes include:
- Exploring the use of AI and ML to aid the estimation & management of QoE in OBM experiences. Can the recent advancements in AI and ML be used to improve our understanding of the perceived quality of object-based experiences?
- Investigating the QoE of new hyper-interactive experiences. How do we measure the importance and the perceived quality of user interaction in OBM experiences?
- Investigating the QoE of new hyper-personalised experiences. What role does personalisation play in terms of the perceived quality?
- Exploring the potential overlap between Perceptual Video Compression (PVC) and OBM QoE. Can a QoE model designed for OBM be utilised to improve video encoding in traditional multimedia delivery methods, aiming towards more sustainable video streaming?
This PhD position offers great flexibility and we welcome the opportunity to explore other ideas & themes around QoE & OBM.
About You
You will have a 1st or 2:1 (Hons) degree in Computer Science (or related field), or a Master's (or equivalent) in a relevant engineering or scientific discipline or equivalent specialist experience. We welcome applicants with a genuine interest in QoE or a related discipline.
Funding
These PhD studentships are open to UK students and cover university tuition fees for 4 years and a tax-free maintenance grant (stipend) of £19,237k per annum. The studentships also include funding to support travel costs to present research at national and international conferences.
Application Process and Next Steps
General enquiries are welcomed by Professor Nicholas Race by email (networkedsystems@lancaster.ac.uk)
Otherwise, you may apply directly: Applying for postgraduate study mentioning the “AI4ME PhD studentship”.
Details
Start Date: October 2024
Deadline for applications: Friday 19th April 2024
Interview Date: To be confirmed
Description
Background: Network security remains a critical challenge in today's world, with the ever-evolving landscape of cyber threats demanding new and adaptable solutions. Traditional rule-based methods for anomaly detection in network traffic struggle to keep pace with the sophistication of attackers, often requiring manual intervention, and lacking the flexibility to adapt to novel attack vectors. This project seeks to address these limitations by leveraging the power of advanced statistical learning, specifically focusing on the field of Explainable Artificial Intelligence (XAI), to develop a real-time anomaly detection system for network Intrusion Detection Systems (IDS).
The challenge: AI techniques have shown great promise in anomaly detection, offering the potential to automatically learn patterns from historical data and identify deviations indicative of malicious activity. However, the sheer volume, velocity, and heterogeneity of network traffic data present significant challenges. Efficient and scalable algorithms are needed to process this data in real-time, while simultaneously ensuring the interpretability and explainability of the results. This is crucial in an IDS setting, where understanding the rationale behind anomaly detection is critical for effective decision-making and maintaining trust in the system.
Project outline: This PhD project aims to build a data-driven approach for real-time anomaly detection in network traffic using XAI techniques. We will begin with a comprehensive review of existing online anomaly detection algorithms, focusing primarily on state-of-the-art XAI techniques while concurrently investigating competing deep learning approaches. At the same time, to gain a deeper understanding of the problem space, we will conduct an in-depth study of the specific challenges and complexities associated with network traffic data, such as its high volume, velocity, and inherent heterogeneity (both of the data streams and of the anomalous events). The goal of the initial analysis will be to evaluate and understand the limitations of current approaches with network traffic anomaly detection.
Drawing upon the insights gained from the comparative analysis and the in-depth study, we will propose a novel methodology tailored specifically for real-time anomaly detection in network traffic. One way is to generalise our recent developments in Statistical Anomaly Detection to work with high-dimensional network data. Those approaches are based on well-defined fast and efficient optimizations that identify unexpected changes in data patterns to answer the question “Are we seeing something significantly different from what has been observed so far?”. This will allow the methodology to handle real-time data streams, while simultaneously ensuring the interpretability of its outputs for informed human decision-making.
The hope would be to evaluate the resulting procedure via a real-world IDS, via a collaboration with the Lancaster University's ISS department, refining the algorithm directly with help of the practitioners.
Broader outcomes: While the primary focus of this project will be on real-time anomaly detection for Lancaster University's IDS, the proposed approach, with appropriate adaptation and consideration for specific domain requirements and data characteristics, has the potential to be generalized to other network monitoring applications beyond intrusion detection and potentially larger-scale scenarios.
The candidate: The ability to work and research independently is highly valued. This project expects strong foundational knowledge in data science, with a specific emphasis on statistical learning, and general understanding of ML. Given the wide scope of the project, in addition to a solid theoretical background, the candidate should have knowledge of both R and python as well as the most popular data manipulation and ML libraries.
For informal enquiries about the project, please contact Gaetano Romano on (g.romano@lancaster.ac.uk) or Bill Oxbury on (w.oxbury@lancaster.ac.uk).
To apply, please send a CV and cover letter demonstrating your motivation for the post to dsi@lancaster.ac.uk.
2. Create your application
You will need to put in an application to the University's online application system. Please follow the University's guidance regarding the required documentation.
Please make sure to include a CV (mandatory, maximum of two pages) including your previous degrees and graduation grades, as well as any relevant skills. Where it applies, also include awards of excellence, publications, and links to code releases, such as through GitHub.
Please follow all of the requirements. Not adhering to these requirements may at best delay the processing of your application, and at worst might result in immediate rejection. The preferred format for all supporting documents is PDF.
2.1. Your Research Proposal
Please note that even if you are applying for a funded PhD position, you will need to develop a proposal.
At the top of the first page of the Research Proposal, please include the following information:
Mandatory
- A clear indication of the SCC research group(s) you want to work with.
- A list of two or three works that are similar to your proposal. This list is in addition to any other references you may wish to include.
Optional
- The names of the SCC academic(s) you want to work with. Please also indicate if you would like us to consider your application if your preferred supervision team is not possible.
2.2. Your Personal Statement
A personal statement is mandatory and should be a maximum of one page. The document should explain your motivation to work on your chosen project and a little about your background.
Other methods of applying for a PhD
Studying for a research degree is a highly rewarding and challenging process. You'll work to become a leading expert in your topic area with regular contact and close individual supervision with your supervisor.
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Doctoral Training Centres
You can also apply for a PhD from one of the Doctoral Training Centres and Partnerships that work with the School of Computing and Communications. Details of each of the Training Centres are provided here.
