Dual-award between The University of Manchester and The University of Melbourne

This project will be based at The University of Manchester, with 12 months spent at The University of Melbourne.  

Project description:

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver condition in developed countries, with a global prevalence of 30%. The severe form of MASLD, MASH (metabolic dysfunction-associated steatohepatitis), is characterised by fat accumulation, inflammation and frequently fibrosis in the liver, and increases the risk of developing life-threatening liver cancer. In addition, 70% of individuals with type 2 diabetes (T2D) have MASLD, and thereby a 2.5-fold greater mortality risk. Despite this global epidemic, the first therapy for MASLD was only provisionally approved for human use in the USA in 2024, and new approaches are urgently needed. 

Lipid accumulation in the liver is a primary driver of MASLD, and various therapies currently in clinical trials focus on reducing lipid accumulation for the treatment of advanced liver disease. Using a comprehensive genetic screen in liver cells, we identified thousands of novel regulators of lipid metabolism highly relevant for therapeutic targeting. 

Based on this genetic screen, this PhD project will focus on the development of machine learning algorithms to predict and pinpoint the most important regulators of lipid metabolism in the liver, and their impact on systemic blood glucose control. Specifically, this PhD project will focus on the development of machine learning approaches to predict the ability of the proteins identified in the genetic screen to regulate lipid accumulation and glucose metabolism in the liver, components of the project that will be carried out at The University of Manchester. This will be accompanied by testing the metabolic impact of the top candidate(s) in cell and/or mouse models of metabolic disease, which will be carried out at The University of Melbourne. Together, this project will forecast the effects of novel candidates on MASLD and T2D progression and will allow for effective therapeutic validation. 

Supervisory Team

We advise that you speak to the Supervisory team before applying for this project. 

• Dr Hongpeng Zhou, University of Manchester (email: Hongpeng.zhou@manchester.ac.uk)

• Dr Magdalane Montgomery, University of Melbourne (email: magdalene.montgomery@unimelb.edu.au)

Apply:

• Find out more and apply now.

This project will be based at The University of Manchester, with 12 months spent at The University of Melbourne. 

Project description:

Offshore wind energy plays a pivotal role in the global transition to sustainable energy. 

To decarbonize its energy supply and meet Net Zero targets, the UK is aiming for a rapid expansion of offshore renewable energy, with plans to deploy 50GW of offshore wind capacity by 2030, including 5GW from floating wind farms. However, efficiently delivering, transporting, and storing this green energy presents significant challenges. Artificial energy islands offer an innovative solution by streamlining the integration of offshore wind farms and minimizing the need for multiple onshore landing points. These islands will facilitate the conversion of green energy into sustainable fuels for aviation and maritime transport. 

Additionally, they are being proposed as sites for green hydrogen production and battery storage, driving a substantial shift in the green energy landscape. A notable example is the North Sea Energy Island, part of the North Sea Wind Power Hub project. This expansive initiative covers 2.5 square miles and features an airport, harbour and converters that transform wind-generated electricity from AC to DC, allowing for transmission to five neighbouring countries. In light of the escalating risks associated with climate change, it is essential that these critical offshore infrastructures are designed for safe operation and resilience against rising sea levels and extreme marine conditions. 

To achieve the ambitious goals for offshore wind energy, it is essential to address the geotechnical challenges associated with building foundations in the unique marine environment of calcareous sand. One key challenge is understanding how moisture distribution within these sandy foundations affects their stability and performance over time. Fluctuations in moisture content, caused by tidal movements and changes in groundwater levels, can lead to uneven settlement, which poses risks to the integrity of offshore structures. Therefore, this project aims to investigate the settlement mechanisms of calcareous sand foundations, focusing on how moisture mitigation strategies can improve their long-term performance. 

The project will employ advanced sensing technologies and a digital twin approach to monitor moisture dynamics and foundation behaviour in real-time. By integrating these innovative methods, we will enhance our understanding of the complex interactions within the foundation system. This will allow us to develop accurate predictive models that can account for moisture fluctuations and their effects on settlement behaviour. The ultimate goal is to create a comprehensive framework that addresses the challenges posed by varying moisture conditions, thus improving the reliability and longevity of offshore foundations. 

The project is part of a joint PhD program between The University of Melbourne and The University of Manchester. The student will spend one year at The University of Melbourne, working with world-class experts in offshore engineering, and the remaining time at The University of Manchester. This collaboration will allow the student to gain a global perspective on the challenges of offshore energy development and access to specialized resources at both institutions. 

Supervisory Team:

We advise that you speak to the Supervisory team before applying for this project. 

• Dr Xiaomin Xu, University of Manchester (email: Xiaomin.Xu@manchester.ac.uk)
• Prof Yinghui Tian, University of Melbourne (email: yinghui.tian@unimelb.edu.au)

Co-supervisors  

The University of Manchester:  

• Majid Sedighi  
• Lie Kong 
• Peyman Babakhani 

University of Melbourne:

• Yifa Wang 
• Shubhrajit Maitra  

Apply:

• Find out more and apply now.

This project will be based at The University of Manchester, with 12 months spent at The University of Melbourne. 

Project description:

Early language skills enable children to learn and predict their educational outcomes. 

Unfortunately, many children start school with limited language and the risk of this is higher for children experiencing socio-economic disadvantage. In order to support children’s language development we need an understanding of the aspects of early interaction that can drive language learning. This project will focus on children’s interactions in their third year of life. While there is evidence that expert ratings of recordings of children’s interaction at this age can be used to predict later language (Down et al, 2015), there is limited understanding as to which aspects of the interactions these ratings are picking up on. The goal of this PhD project is to use statistical analysis of observational data to identify the individual aspects of children’s interaction that drive language development. The student will oversee the collection of a) home videos of children interacting with their caregivers at 24 months of age b) videos of the same children interacting with preschool educators at 24 months and c) home videos of child language during play at 30 months. Supported by expert training, they will code the videos for language and communication. Recent work by the supervisory team used statistical analysis to identify the combinations of child and caregiver behaviours in videos collected at 12 months of age that are the best predictors of language development of the second year of life (Donnellan et al, 2019). The goal of this work will be to extend this for interactions during the third year – to identify the behaviours in the 24-month recordings that are potential drivers of language ability at 30 months. A challenge in analysing interaction is that the behaviours of the child and the adults they are interacting with are not independent. That is, it can be hard to determine the extent to which a given child is eliciting the language they hear and thereby propelling their own development. Analysing separate videos of each child interacting with two different adults presents the opportunity to determine which aspect of the child’s development is driven by the child, which by their caregiver and which by other interactional partners. 

The project will involve the use of time-series modelling. To date, all work in this area has built statistical models based on counts of behaviours identified in videos. This approach requires the researcher to determine in advance what combinations of behaviours to count, constrained by the need to avoid a combinatorial explosion. The current work will improve on this by analysing the video transcriptions directly – treating them as behaviours unfolding across time and analysing them using neural time series models. Sequences of coded behaviours that are associated with variance in later language ability will be identified directly in the data. The student will be able to benefit from courses on the new MSc in Computational and Corpus Linguistics at The University of Manchester in order to acquire the requisite statistical analysis skills. 

Supervisory Team:

We advise that you speak to the Supervisory team before applying for this project. 

Dr Colin Bannard, University of Manchester (email: colin.bannard@manchester.ac.uk )

Dr Penny Levickis, University of Melbourne (email: penny.levickis@unimelb.edu.au )

Co supervisors  

The University of Manchester  

Professor Danielle Matthews  

The University of Melbourne  

Dr Hannah Stark  

Apply:

• Find out more and apply now.

Urban digital twins for sustainable cities: Exploring collaborative innovation and value creation 

Project description:

Cities worldwide are grappling with complex sustainability challenges that require innovative solutions. Urban digital twins – virtual replicas of cities that simulate real-world conditions – offer a promising approach to overcome data silos and address the intricate interdependencies hindering urban innovation. However, developing these systems faces two significant challenges: the need for coordinated adaptation of complex technological systems under various constraints, and the requirement for cooperation among diverse stakeholders with their own goals and interests. 

This research project aims to tackle these challenges by investigating how urban digital twins can drive sustainable urban development through collaborative innovation. The study will explore three key questions: 

1. How do organisations create and capture value in urban digital twin ecosystems? 
2. How can governance structures in these ecosystems effectively balance scalability and innovation? 
3. How can urban digital twins catalyse cross-sector collaboration for urban sustainability? 

To answer these questions, the project will combine the supervisors’ expertise in collaborative innovation, emergent digital technologies, urban infrastructure, and technology diffusion. The research will involve extensive fieldwork in both Manchester, UK and Melbourne, Australia, engaging with various stakeholders in urban digital twin initiatives. This international comparison will provide insights into how different urban contexts impact value co-creation and ecosystem governance. 

The project will employ a mix of qualitative and quantitative methods, including interviews, case studies, and potentially system dynamics modelling. It will explore innovative business models that encourage collaboration while allowing individual organisations to capture value. The research will also examine how governance structures evolve as digital twin initiatives scale up, and how they can balance centralised control with decentralised innovation. 

Furthermore, the study will investigate how urban digital twins can break down silos, facilitating dialogue between technical experts, policymakers, businesses and citizens. It will explore how these platforms enable collaborative design processes that address both technical and social aspects of urban sustainability. 

The project offers a unique opportunity for a PhD candidate to engage with cutting-edge research at the intersection of digital innovation, urban planning, and sustainability. 

The successful candidate will spend time at both The University of Manchester and The University of Melbourne, benefiting from world-class expertise and research networks at both institutions. 

This research has significant implications for both theory and practice. It will contribute to academic debates on ecosystem governance, collaborative business models, and the role of digital platforms in urban innovation. For practitioners and policymakers, it will provide insights into designing effective governance structures and value creation mechanisms for urban digital twin platforms. 

By bridging the gap between technological potential and practical implementation, this project aims to unlock the full potential of urban digital twins in creating more sustainable, resilient and liveable cities. The findings will be valuable for city planners, technology companies, policymakers, and anyone interested in the future of urban development and digital innovation. 

Supervisory Team:

We advise that you speak to the Supervisory team before applying for this project. 

• Professor Adrien Querbes, University of Manchester (email: adrien.querbes@manchester.ac.uk)

• Professor David Keith, University of Melbourne (email: david.keith@unimelb.edu.au)

Apply:

• Find out more and apply now.