Details

Start Date: April 2024 or October 2024.

Deadline for applications: Open-ended: please apply as soon as possible

Interview Date: TBC

Academic Requirements: First-class or 2.1 (Hons) degree, or Master's degree (or equivalent) in an appropriate subject.

Description

A fully funded PhD iCASE studentship for UK applicants, including fees and enhanced stipend is available in the School of Computing & Communications at Lancaster University. The studentship is supported by British Telecom (BT) and will be jointly supervised by Lancaster University and BT.

Cloud rendered immersive Extended Reality (XR) has many applications across consumer, enterprise, and industry domains. Cloud gaming enables consumers to play immersive games on a variety of client devices by streaming content rendered from remote GPU servers. Similarly, industry use cloud rendering to process and visualise complex digital twin data for monitoring, control, and simulation.

To provide the best possible Quality of Experience (QoE) for cloud rendered XR, future networks will need to be optimised to provide high bandwidth and ultra-low latency connectivity on demand. Programmable networks which can be dynamically configured and controlled through software, will enable greater flexibility, automation, and customisation in managing network resources, to provide the best possible QoE at the lowest cost. New capabilities such as Low Latency, Low Loss, and Scalable Throughput (L4S) congestion control and distributed cloud compute will further be leveraged to optimise the delivery and QoE of cloud rendered XR experiences.

While there are well-established standards and techniques for measuring the perceptual quality of individual audio-visual components, there is limited work studying methods for measuring the perceived quality of cloud rendered immersive XR experiences across different use cases and contexts such as devices, applications, networks, and cloud. Hence, there is a need to combine established QoE techniques with new studies on the perceptual quality of immersive XR, considering future programmable networks and evolving content delivery technologies.

What will the PhD involve?

• Background research into the state of the art in the perceptual quality measurement of streamed games and immersive XR experiences.
• Background research into the streaming of immersive XR services over future programmable networks that enable delivery at required QoE.
• Experimental identification of the parameters of immersive games and XR experiences which affect perceptual quality.
• Development and evaluation of new perceptual quality measures for games and immersive XR experiences.
• Development and evaluation of tools that automatically measures and predicts the quality of a streamed immersive XR experience delivered over a programmable network, which can be used to optimise cost of provision for a required QoE.

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, and ideally experience, in topics such as cloud computing, extended reality (XR) and quality of experience (QoE). 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

This PhD studentship is open to UK students and covers university tuition fees for 4 years and a tax-free maintenance grant (stipend) of over £20k per annum. The studentship also includes funding to support travel costs to present your 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 “BT Cloud XR iCASE PhD studentship”.

Details

Start Date: January 2024 or April 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 £18622k 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: January 2024 or April 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 £18622k 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 an anomaly detection is critical for effective decisionmaking 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 decisionmaking.

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.