Join a research project

This is a self-funded opportunity. 

Project advert

Metaverse is an interconnected multi-party platform enabling geographical dispersed users to engage in real-time across multiple virtual spaces. Current Metaverse technologies relies heavily on ubiquitous computations and pervasive network access for multi-modal 3D reconstructions and real-time dissemination. However, existing distributed computing techniques such as edge-fog computing and network protocols like TCP/IP/QUIC are insufficient for the high computational demands and real-time transmission, resulting in issues such as intermittent connectivity and delays, hindering immersive experience.

This PhD project aims to develop a novel intelligent communication system that integrates Metaverse-specific information, such as 3D virtual object data, users’ movements, and computational needs, directly into the network layer. By utilising deep reinforcement learning (DRL), specifically deep Q-networks (DQN), this system will optimize in-transit computations providing ubiquitous computations and select optimal congestion free routing path to enable real-time, multi-party communication with sub-20ms latency.

The research will be validated through a remote physiotherapy usecase, which requires large-scale 3D reconstructions and real-time data dissemination. This project extends beyond remote physiotherapy, with applications in remote surgeries, virtual learning and business, among others.

The research will be conducted in Manchester Met’s new state-of-the-art Dalton Building, which encompasses world-class research laboratories (a dedicated future-network/IoT lab for this project), and teaching spaces.

Project aims and objectives

The aims of this PhD project are to develop and validate an intelligent, compute-oriented communication system that enables ubiquitous computations and enhances the real-time, multi-party communication capabilities of the Metaverse. In so doing, this research will address the limitations of current distributed computing techniques and network protocols for Metaverse by integrating 3D virtual object data and computational requirements directly into the network layer, enabling seamless and immersive user experiences with sub-20ms latency.

Specific requirements of the candidate

In addition to standard entry requirement, the ideal candidate should possess the following qualifications, skills, knowledge, and experiences.

Essentials

• A good understanding of networking protocols such as TCP/IP/QUIC and disturbed computing techniques such as Edge/Fog/Cloud computing.
• Proficiency in programming languages such as Python, C#, or C++​.

Desirables

• A basic understanding and familiarity with Artificial Intelligence techniques such machine learning or deep learning, to name a few.
• Hands-on experience developing Metaverse/Virtual Reality applications using Unity3D, Unreal Engine, or any other similar platform.
• Experience with VR hardware such as Meta Quest, HTC Vive, or similar devices is a plus.

How to apply

Interested applicants should contact Muhammad Atif Ur Rehman ([email protected]) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in in Computing and Digital Technologies. Alternatively, you can download the PGR application form.

You should also complete the PGR thesis proposal and a narrative CV (supplementary information) form addressing the project’s aims and objectives, demonstrating how the skills you have map to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to [email protected].

Closing date: 14 October 2024. Expected start date: January 2025 for Home students and April 2025 for International students. 

Please quote the reference: SciEng-2024-Communication-Metaverse

This is a self-funded opportunity.

Project advert

Air pollution remains a critical environmental and public health issue. Accurate and fine-grained estimation of vehicle emissions is essential for informed, effective mitigation strategies (e.g., targeted promotion of alternative transportation in urban environments). Current methods for air quality estimation present limitations such as coarse spatial granularity and high costs.

Our proposed method uses ground vibration, captured by affordable seismic devices, as a proxy for emissions. We employ machine learning, timeseries, and signal processing techniques for detecting, quantifying, and classifying vehicles based on imprints left on seismic signals, which allows us to obtain estimates of harmful vehicle emissions. We have carried out preliminary work that successfully estimates traffic volume with spectral analysis of ground vibration signals. This PhD project will extend that work, with use of multimodal data (e.g., tabular/signal/images) and Computer Vision techniques.

This project provides an exciting opportunity to be based in our new £117M Dalton Building, equipped with state-of-the-art computing labs and interactive study spaces. A successful candidate will join a hands-on multidisciplinary team of machine learning and computer vision practitioners, and geophysicists. The project will likely involve key stakeholders such as Transport for Greater Manchester (TfGM) and will promote a substantial impact on sustainability.

Project aims and objectives

The aim of the project is to estimate vehicle emissions harmful to the environment and human health and to stratify the contribution of vehicles to harmful emissions in urban environments, where transportation is a significant source of pollutants.

The project will produce several key contributions to knowledge, including:

1. The development of models for vehicle classification based on analysis of spectral images,
2. Models estimating air quality using integrated seismic and environmental data, and
3. A comprehensive dataset linking traffic characteristics with air quality metrics.

These outcomes will be disseminated through high-impact journal publications, with plans for at least four papers in top-tier journals such as IEEE Transactions on Signal Processing and IEEE Transactions on Neural Networks and Learning Systems. 

Specific requirements of the candidate

Ideally the candidate will have:

Essential

• A first or upper-second class BSc honours degree in a STEM subject with a strong computational component (e.g., Computer Science, Physics).
• Some practical experience with computational science and machine learning with tools and languages such as Python and its standard scientific stack (e.g., sklearn, numpy, scipy, matplotlib).
• Experience with tabular, timeseries, signal, and/or image data.

Desirable

• An MSc in a related discipline will be looked favourably on but is not essential.
• Good written and oral communication skills.
• An ability to critique and analyse scientific methodology and data.

How to apply

Interested applicants should contact Dr Rochelle Taylor for an informal discussion at [email protected].

To apply for a full-time PhD in Computing and Digital Technology, you will need to complete the online application form or download the PGR application form.

You should also complete the PGR thesis proposal and a Narrative CV (supplementary information) form addressing the project’s aims and objectives, demonstrating how the skills you have map to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section or email the application form and statement to [email protected].

Closing date: 14 October 2024. Expected start date: January 2025 for Home students and April 2025 for International students. 

Please quote the reference: SciEng-2024-Urban-Vehicle-Emissions

This is a self-funded opportunity.

Project advert

This PhD project at Manchester Metropolitan University focuses on developing a hybrid cryptographic framework that integrates classical cryptography with post-quantum cryptography (PQC) and artificial intelligence (AI). As quantum computing advances, it threatens the security of traditional cryptographic systems, making the transition to quantum-safe infrastructures essential. This robust and future-proof security framework provides immediate protection against current threats while ensuring resilience against potential quantum attacks. The project will involve designing a hybrid model that combines the strengths of both classical and quantum-safe cryptography, with AI playing a critical role in dynamically managing and optimizing security protocols.

The research will be conducted in Manchester Met’s state-of-the-art new Dalton Building, which houses world-class laboratories, teaching spaces, and research hubs at the heart of University’s campus. This environment fosters innovation and collaboration across science and engineering disciplines, providing the ideal setting for cutting-edge cryptographic research. The project will leverage Manchester Met’s advanced facilities and expertise in cybersecurity, cryptography, and AI, with additional external collaboration with Queen’s University Belfast that will provide access to research facilities and expertise, particularly in PQC. The outcomes of this research have the potential to influence global security standards and contribute significantly to the field of cryptography in the quantum era.

Project aims and objectives

Aims:

The primary aim of this PhD project is to develop a hybrid cryptographic framework that combines classical cryptography, post-quantum cryptography (PQC), and artificial intelligence (AI) to ensure secure communications in the era of quantum computing. The framework will be designed to provide immediate protection against existing threats while being resilient to future quantum attacks.

Objectives:

Integrate Classical Cryptography and PQC: Design and develop a hybrid cryptographic framework that effectively combines traditional cryptographic methods with post-quantum algorithms.

Incorporate AI for Adaptive Security: Develop AI-driven algorithms that dynamically manage and optimize the cryptographic framework, adapting to evolving security threats in real-time.

Validate the Framework: Test and validate the framework using real-world scenarios and datasets to ensure its robustness and effectiveness against both classical and quantum threats.

Contribute to Global Security Standards: Explore the framework’s potential to influence the development of international security standards for quantum-safe communications.

Specific requirements of the candidate

In addition to meeting the standard entry requirements (a minimum of an honours degree at first or upper second class level), applicants for this PhD project may possess the following:

• Proficiency in programming languages such as Python, C++, or Java, particularly in the context of cryptography and AI.
• Knowledge of cryptographic algorithms, both classical and post-quantum, and their practical applications.
• Experience with machine learning or artificial intelligence, particularly in areas related to security and optimisation.

How to apply

Interested applicants should contact Dr Safiullah Khan ([email protected]) for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computing and Digital Technologies (or download the PGR application form).

You should also complete the PGR thesis proposal and a Narrative CV (supplementary information) form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to [email protected].

Closing date: 14 October 2024. Expected start date: January 2025 for Home students and April 2025 for International students. 

Please quote the reference: SciEng-2024-Cryptography

This is a self-funded opportunity.

Project advert

Digital ulcers are common in patients with systemic sclerosis (SSc), occurring in up to 50% of patients, and often early in the disease course. They can be very difficult to heal, and often become infected, sometimes with underlying osteomyelitis (bone infection) which can necessitate amputation.

Clinical trials assessing SSc-related digital ulcers have been hampered by a lack of reliable outcome measures of healing. The current treatment decision was dependent on the clinician, which could lead to surgery or simply medication. Recent studies showed that patients with SSc could collect high-quality smartphone images of their digital ulcers as a first step in a smartphone-based outcome measure.

We are looking for candidates to research non-invasive 3D reconstruction techniques (photogrammetry) to reconstruct difficult human structures, such as SSc lesions on hands. This will then be added to a newly create mobile application for SSc patients to use.

The PhD candidate will benefit from training at Manchester Metropolitan University within our brand-new facilities in the £117M Dalton Building – the Faculty of Science and Engineering’s new home. They will also gain insight from the Scleroderma and Raynaud’s Research Group within the Salford Royal NHS Foundation Trust.

Project aims and objectives

The aim of this project is to accurately reconstruct hands of systemic sclerosis patient with digital ulcers using computer vision and non-invasive photogrammetry. To achieve this aim, the main objectives are:

1. 3D reconstruct human hands using photogrammetry;
2. Evaluate accuracy of photogrammetry against LiDAR-based reconstruction;
3. Create a mobile application that can photograph hands of SSc patients and reconstruct with ulcerations;
4. Accurately measure digital ulcerations using reconstructed hands and novel AI algorithms;
5. Validate performance of the 3D reconstructed AI measurements in a clinical setting and against manual clinician annotation.

Specific requirements of the candidate

Candidates must have a strong motivation for research and excellent programming skills.

Experience in mobile application development and 3D reconstruction techniques is essential. Expertise of developing computer vision and machine learning algorithms would be desirable, with an interest in image analysis.

Qualifications

• A high-grade undergraduate degree (first class or upper second) in Computer Science or MSc in related field.

Skills

• Mobile application development (.NET MAUI or similar).
• 3D reconstruction (photogrammetry).
• Knowledge of software development and programming.
• Good communication and writing skills.
• Developing image analysis/machine learning algorithms would be beneficial.
• Able to work as part of a joint academia and clinical team.

How to apply

Interested applicants should contact Dr Adrian Davison for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computing and Digital Technologies. Alternatively, you can download the PGR application form.

You should also complete the PGR thesis proposal and a narrative CV (supplementary information) form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to [email protected].

Closing date: 14 October 2024. Expected start date: January 2025 for Home students and April 2025 for International students. 

Please quote the reference: SciEng-2024-Digital-Ulceration

This self-funded project is a full-time opportunity open to home and international students. 

Project summary

Over the years, tornadoes (and to a lesser extent, downbursts) have attracted a great deal of attention both from the engineering community and the media. Tornadoes are rather complex phenomena which despite considerable research efforts are not particularly well understood. This tends to be due to two main reasons: firstly, the high wind speeds associated with the strongest tornadoes makes it difficult to obtain near ground measurements both from a safety perspective and from an equipment perspective – standard measuring equipment simply cannot withstand the corresponding wind-induced forces. Secondly, tornadoes are unpredictable and can be of a relatively small scale (~10m - 1km). Thus, the probability of correctly predicting a tornado’s location is rather small, although in general terms predicting their occurrence over large areas is reasonably straightforward.

This project addresses these issues directly by using trees and crops as widespread damage indicators; the failure patterns arising from these plants when subject to a tornado/downburst can provide considerable information relating to tornado/downburst. Considerable progress has been made on this front, but all this research contains several fundamental assumptions which has never been validated. This research will test all these assumptions through detailed wind tunnel measurements in large-scale wind tunnel.

Project aims and objectives

The aim of this research is to investigate if existing tornado models can predict realistic tree/crop fall patterns.  This will be achieved by analysing a series of controlled model-scale experiments undertaken in partnership with colleagues at Western University. Underpinning this aim are 4 objectives:

1. To undertake a critical literature review relating to tree and crop fall patterns arising from tornadoes.
2. To analyse experimental data from a variety of physical tests undertaken in a large-scale tornado generator.
3. To compare the results of (2) with patterns arising from analytical tornadoes models.
4. Based on (2) and (3), to develop an appropriate analytical model / propose changes to existing analytical models to provide an accurate insight into tornado scales based on full-scale tree/crop fall patterns.

Specific requirements of the candidate

Applicants should have either an excellent undergraduate or MSc in either engineering, mathematics or physics.

How to apply

Interested applicants should contact Professor Mark Sterling at [email protected] for an informal discussion, providing a copy of your CV and a short statement outlining why you are attracted to this topic.  Please quote ‘Tornado-forest PhD’ in all correspondence.

To apply you will need to complete the online application form for a full-time PhD in Engineering, or download the PGR application form.

You should also complete the PGR thesis proposalform addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section or email the application form and statement to [email protected].

Applications from those who wish to explore the possibility of flexible working arrangements are welcome.

Please quote the reference: SciEng-MS-2024-Tornado-forest

Self-funded Master’s by Research opportunity

Summary

Patients with chronic kidney disease (CKD) are at increased risk of cardiovascular disease events but the precise mechanisms leading to this remain unknown. One key feature in CKD is reduced capacity of the vessels to dilate due to increased levels of reactive oxygen species within the vessel wall and lining endothelial cells. Increased activity of the cytochrome P450 enzyme, CYP1B1, and generation of vasoconstrictor mediator 20 HETE, can contribute to oxidative stress. We have recently demonstrated that the potent antioxidant and CYP1B1 inhibitor, Tetramethoxystilbene (TMS) can restore dilator capacity in an ex vivo model of hypertension, via potentiation of nitric oxide (Zaabalawi et al 2019). Whether TMS can be vasculo-protective and hence prevent CKD progression is not known.

This one-year project will allow the student to learn the skill of maintaining endothelial cells in culture and perform biochemical assays to determine the oxidative state of the cells and the mediators released. Additionally, skills in microdissection and physiological function measurements of isolated blood vessels will be gained. The student will be supervised by experts in the field and encouraged to communicate findings at conferences. They will be mentored to support their career progression and employment prospects.

The successful candidate will be based within the Centre for Bioscience Research Centre at Manchester Met.

Aims and objectives

The project aims to determine the vasculo-protective effects of CYP1B1 enzyme inhibitors in the prevention of disease progression in chronic kidney disease (CKD) patients. The objectives are to:

• determine the influence of CKD sera on endothelial cell viability and oxidative state, using cell culture and biochemical assays, in the presence or absence of the enzyme inhibitors.
• determine the level of vasoactive mediators released by the endothelial cells after exposure to CKD sera, using biochemical analysis.
• determine the pharmacological influence of CKD serum derived mediators on vascular reactivity of isolated arteries, using pressure myography.

Specific requirements of the project

A minimum honours degree at first or upper second class level. Exposure to microdissection or cell culture is desirable but it’s not essential

Student eligibility 

This self-funded Master’s by Research opportunity is for one year (full-time) and is open to international or home applicants.

How to apply

For an informal discussion regarding the requirements of the position, please contact Dr May Azzawi at [email protected]. To apply, please  complete the PGR application form, indicating how you meet the essential and desirable criteria in the personal statement. Submit your  application form by email to [email protected]. 

Please quote the reference: SciEng-MA-2022-enzyme-inhibitors

Self-funded Master’s by Research opportunity

Summary

Ageing is associated with an attenuation in peak heartrate, exercise hyperaemia and reduction in overall exercise capacity. Several mechanistic reasons have been proposed for these changes which include but are not limited to altered neural control of cardiovascular system, altered production and sensing of vasoactive metabolites and changes to skeletal muscle profile. Nonetheless, several important mechanistic research questions remain unanswered. Using human models, this research project will utilise in-vivo and ex-vivo techniques to further investigate the relative influence of lifestyle, exercise type / duration, vasoactive metabolites on the age-associated changes to cardiovascular / neurovascular response to exercise.

The successful candidate will be based within the Centre for Bioscience Research Centre at Manchester Met.

Specific requirements of the project

A highly dedicated, enthusiastic and well organised student with at least an upper second class honours degree in Human Physiology, Biomedical Sciences, Sports Sciences or a related discipline is sought to undertake this research project.

Students with a strong background and interest in cardiovascular and exercise physiology are encouraged to apply. Experience in some of the research techniques (Duplex Doppler Ultrasonography, venous blood sampling and biochemical analysis) and/or experience in participant recruitment studies is desirable but not essential.

Further training in in-vivo and ex-vivo research techniques will be provided. This project will involve working with study participants so good social skills will be advantageous.

Student eligibility 

This self-funded Master’s by Research opportunity is for one year (full-time) and is open to international or home applicants.

How to apply

For an informal discussion regarding the requirements of the position, please contact Dr Rehan Junejo at [email protected]. To apply, please complete the PGR application form, indicating how you meet the essential and desirable criteria in the personal statement. Submit your  application form by email to [email protected]. 

Please quote the reference: SciEng-RJ-2022-healthy-ageing-exercise

Self-funded Master’s by Research opportunity 

Summary 

Liver fibrosis kills and is an increasing disease burden. Despite efforts to define pro-fibrotic processes there are still no approved anti-fibrotic therapies for liver fibrosis. Hepatic Stellate Cells (HSCs) are well established as the cellular drivers of liver fibrosis. In response to inflammatory and mechanical cues they drastically alter their phenotype to become proliferative, migratory, and contractile myofibroblasts. A key characteristic is secretion of fibrillar collagen which is the main component of the fibrotic scar. 

Mechano-signalling: Mechanical cues can promote HSC activation. As fibrosis progresses the liver’s mechanical properties change, and tissue stiffness increases. It has been shown that mechano-signalling can occur very early in disease and is an important driver of HSC activation. However, it is not well understood how external mechanical cues are able to drive fibrosis. 

PDLIM5 (PDZ And LIM Domain Protein 5) is thought to function as a cytoskeleton associated signalling scaffold or platform, where regulatory enzymes and their substrates are brought together in response to external mechanical cues. This project will characterise and interrogate the proteins that interact with PDLIM5 in HSCs to identify novel therapeutic targets for liver fibrosis. 

The successful candidate will be based within the Centre for Bioscience Research Centre at Manchester Met. 

Project aims and objectives 

Dr James Pritchett has previously shown that mechano-regulated activation of hepatic stellate cells (HSCs) drives liver fibrosis in a mechanism involving Yes Associated Protein 1 (YAP1)^{1 2}. It remains to be elucidated precisely how external mechanical cues are able to alter YAP1 activity in HSCs. It has recently been shown³ in a human colon epithelial cell line (Caco-2) that mechano-sensitive YAP1 activity can be regulated by interactions with PDZ and LIM Domain Protein 5 (PDLIM5). PDLIM5 in HSCs may function as a mechano-sensitive platform that is a site of interaction between nuclear factors and regulatory enzymes such as kinases. 

Hypothesis: PDLIM5 is required for mechano-activation of Hepatic Stellate Cells. 

Objectives: 

1. Investigate PDLIM5 expression during HSC activation. 
2. Generate inducible cell lines to interrogate PDLIM5 function. 
3. Generate reporter cell lines to investigate the regulation of pro-fibrotic YAP1. 

Outcomes: Stable cell lines for the interrogation of PDLIM5 function and assays of YAP1 activity. The student will develop advanced laboratory skills and contribute to publications describing: 

1. The role of PDLIM5 in HSCs; and 
2. The regulation of YAP1 activity during fibrosis.  

This research project aligns with the strategic goals of the Centre for Bioscience and falls under the theme of Ageing and Lifelong Health, but also has links to the Cardiovascular theme. 

References: 

1. Athwal VS*, Pritchett J*, et al. EMBO Mol Med 2017 
2. Martin K*, Pritchett J*, et al. Nat Commun 2016 
3. Elbediwy A, et al. J Cell Sci 2018 

Specific requirements of the project

• Upper second, or first class, honours degree in Biomedical Science or related subject. 
• Previous experience of working in a cell and molecular biology research laboratory. 

Skills/techniques the ideal candidate will already have some experience or knowledge of: 

• Mammalian cell culture 
• RNA isolation and reverse transcription 
• qPCR 
• Western blot 
• Immunofluorescence 
• Transfections 
• Cloning and vector prep 

Student eligibility

This self-funded Master’s by Research opportunity is open to international or home applicants. 

How to apply

For an informal discussion regarding the requirements of the position, please contact Dr James Pritchett at [email protected]. To apply, please complete the PGR application form, indicating how you meet the essential and desirable criteria in the personal statement. Submit your application form by email to [email protected].  

Please quote the reference: SciEng-JP-2022-pdlim5-function 

Self-funded PhD opportunity

Summary

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and has an increase in incidence and prevalence with each decade of adult life. Over 6 million people in Europe suffer from AF.  It is responsible for increased risk of death, stroke, thromboembolic complications, tachyarrhythmic cardiomyopathy and the development of heart failure (HF).  HF is also a major public health problem that affects over 25 million patients worldwide. It causes morbidity, death, and health-care expenditure globally and despite major advances in pharmacotherapy, our understanding of its underlying disease mechanisms from epidemiological, clinical, pathophysiological, molecular, and genetic standpoints remains incomplete. HF and AF frequently coexist. AF represents the most common arrhythmia of HF patients (approximately 13% of patients in the age range 35 to 64; and 21% of patients aged 65 years or older). On the other hand, HF is a major promoter of AF, increasing the risk of developing AF by approximately five-fold.

The overarching aim is to elucidate pathophysiological mechanisms of coexistent AF and HF, and to use biophysically detailed models predictively to explore potential therapeutic strategies.

Aims and objectives

The mechanism(s) underlying the genesis of AF in HF patients and HF in AF patients remains unclear. This project proposes to investigate and elucidate the underlying pathophysiological mechanisms of this duality and potential therapeutic strategies. This will be done by developing novel AI-enabled electromechanical (as opposed to the usual electrophysiology only) cellular, tissue and organ models for these conditions.

Specific requirements of the project

• A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent) in a quantitative discipline such as engineering, physics, computer science or mathematics.
• A good mathematical background and programming skills in at least one of C/C++, Rust, Julia or Python.
• Experience of numerical methods and machine learning will be beneficial.
• A keen interest in high-impact research work at the interface of physics, engineering, computer science and medicine.

Student eligibility 

Home and international students can apply for this self-funded position.

How to apply

For an informal discussion regarding the requirements of the position, please contact:

• Dr Ismail Adeniran at [email protected]
• Professor Hans Degens at [email protected]).

Apply online for a Full-time PhD in Computing and Digital Technology or complete the PGR application form, indicating how you meet the essential and desirable criteria in the personal statement. Submit your application form by email to [email protected].We accept year-round applications.

Please quote the reference: SciEng-IA-2022-pathophysiological-mechanisms

Self-funded PhD opportunity

Summary

This proposal will develop algorithms and software to improve ultrasound breast lesion detection and recognition. This will be achieved using computer vision to build up a real-time scanning system, alerting the user of the appearance and type of suspicious lesions. The research will improve state-of-the-art methods by providing the location of the lesion, 3D lesion structure and recognition of the lesion.

This proposal is novel as it aims to overcome the problem of inconsistency in the human operator using real-time ultrasound scanning. With the involvement of clinical and commercial partners, we will adopt the user-centred design for real-life usage. We will conduct clinical testing during the lifetime of the project. A low-cost, targeted, non-invasive ultrasound breast lesion detection system would have substantial health benefits. This would inevitably result in great long-term societal and economic benefits due to the improved quality of life and health of women in an ageing population.

This improved breast ultrasound diagnostic tool will ensure that healthcare professionals can provide a higher level of care for all patients with breast cancer risks, identifying early signs of breast cancer and referring on to other relevant clinicians.

Aims and objectives

The current performance of computer vision research in breast ultrasound imaging has a number of limitations, including dependence on the human operator, and the lack of standardised datasets and algorithms producing high false positive rates. A solution using real-time processing methods to overcome these limitations is important and necessary. This proposal will greatly improve the research field by providing new datasets annotated by radiologists and new methods for real-time breast ultrasound lesion detection and recognition. The goal of this research is to provide fast and reliable tools for the early detection of malignant lesions. The objectives are:

• Document user requirements and design the data collection tool
• Acquire new ultrasound image sequence datasets with clinical reports, i.e., the location of the lesion and the type of the lesion
• Design and optimise algorithms for real-time lesion detection and segmentation of ultrasound image sequences
• Enhance the lesion recognition technique by using the fusion of 2D and 3D features using machine learning algorithms
• Validate the results with clinical decisions and conduct clinical testing 

Specific requirements of the project

Candidates must have a strong motivation for research and excellent programming skills. Expertise of developing computer vision and machine learning algorithms would be desirable, with an interest in image analysis.

Qualifications

• A high-grade undergraduate degree (first class or upper second) in computer science
• An MSc level in Computer Science would be desirable for this post

Skills

• Knowledge of software development and programming
• Good communication and writing skills
• Developing image analysis/machine learning algorithms would be beneficial
• Able to work as part of a joint academia and industry team

Eligibility 

Home and international students can apply for this self-funded position.

How to apply

For an informal discussion regarding the requirements of the position, please contact Professor Moi Hoon Yap at [email protected].

Apply online for a Full-time PhD in Computing and Digital Technology or a Part-time PhD in Computing and Digital Technology. You can also complete the PGR application form, indicating how you meet the essential and desirable criteria in the personal statement. Submit your application form by email to [email protected]. We accept year-round applications.

Please quote the reference: SciEng-MHY-2022-breast-ultrasound

Self-funded Research Masters opportunity

Summary

Dietary and/or caloric restriction is known to be effective in weight loss and methods such as intermittent fasting are becoming increasingly popular amongst the general population. Research is also increasing on the effect of dietary strategies on appetite regulation and metabolic health. A number of gut-derived hormones that are involved in the regulation of appetite are also known to regulate gastrointestinal motility, and thus appear to be intrinsically linked. The effect of different dietary/caloric restriction methods on gastrointestinal function and food and energy intake is an important mechanistic consideration to their effectiveness.

Aims and objectives

The aim will be to investigate the effect of different dietary/caloric restriction strategies on gastrointestinal function and appetite regulation.

Specific requirements of the project

Applicants will have, or expect to gain, a BSc (Hons) degree (2.1 or above) in human physiology, nutrition, bioscience or another related discipline.

The candidate should have excellent analytical, organisational, and communication skills. The ability to work in a team, be flexible with working hours, and have self-motivation are also required.

Experience in working with human participants within a laboratory, health or clinical setting is essential. Experience in dietary analysis and/or physical activity monitoring will be advantageous. Experience in biochemical analysis is also desirable, but training will be provided.

Student eligibility 

Home and international students can apply for this self-funded position.

How to apply

For an informal discussion regarding the requirements of the position, please contact Dr Adora Yau at [email protected] or Dr Gethin Evans at [email protected].

To apply, complete an online application form for either a Full-Time or Part-time position.

You can also complete the PGR application form, indicating how you meet the essential and desirable criteria in the personal statement.

Submit your application form by email to [email protected]. We accept year-round applications.

Please quote the reference: SciEng-AY-2022-gastrointestinal-function