La Trobe University - Sheffield Hallam University Joint PhD Program Scholarship

Updated: 5 months ago
Location: Melbourne, VICTORIA
Deadline: 30 Apr 2023

La Trobe University is offering a graduate research scholarship for students to undertake a joint PhD with Sheffield Hallam University, UK.

Applications for this scholarship are now open to Australian or New Zealand citizens or Australian permanent residents newly enrolling in a PhD. The application deadline is 30 April 2023.

Students undertaking the joint PhD program will be enrolled in a PhD at both institutions. Your supervisory team will comprise of academic staff from both institutions who will provide support and guidance throughout your research. As a student enrolled at both La Trobe and Sheffield Hallam, you will have access to services and support provided by both institutions, including a range of professional and personal development programs.

You will begin your studies at La Trobe University where you will spend the majority of your time, but with an expectation that you will spend typically 12 months at Sheffield Hallam University. Travel to and study at the host institution will be subject to the usual immigration requirements.

The joint PhD includes a tailored program of progress monitoring to fulfil the requirements of both institutions. All candidates will write and submit a thesis for defence by oral examination. On successful completion of the program requirements, you will be awarded a PhD jointly by both institutions.

The successful applicant can commence at any time between 1 July and 30 November 2023, at a La Trobe University campus, and willing to spend typically 12 months based in Sheffield, UK.

Available projects

There are a number of joint PhD scholarship available to applicants from the range of projects listed below, competitively awarded and selection is based on academic merit and suitability to the selected project. Please contact the lead supervisor for more information about these projects.

Project: Modifiable factors associated with post-traumatic knee osteoarthritis in runners following knee surgery

Lead Supervisor: Dr Benjamin Mentiplay

Other Supervisors: Prof Kay Crossley (LTU), Marcus Dunn (SHU), Ben Heller (SHU)

Knee osteoarthritis occurs in an alarming number of young adults who have previously undergone knee surgery. One potential contributing factor to this accelerated development of osteoarthritis is participation in high-impact exercise, such as running, which is often taken up post-surgery as an alternative to contact/twisting sports. This PhD project aims to investigate modifiable factors (e.g., weekly training load or movement patterns) that are associated with knee osteoarthritis outcomes (MRI features and symptoms) in runners who have had knee surgery. The successful candidate will be supervised from leading experts across an international collaboration between La Trobe University and Sheffield Hallam University.

Project: Internet of Medical Things Home Medication Assistant with Adaptive Intelligence

Lead Supervisor: A/Prof Simon Egerton

Other Supervisors: Dr Carina Chan (LTU), Dr Marjory da Costa Abreu (SHU), Dr Carlos da Silva

One third of referrals received from General Practitioners (GPs) and other health professionals to a large regional community nursing service include daily support for oral medication management. The visiting nurse conducts a comprehensive assessment inclusive of medication and develops a care plan in consultation with the client. Medication reviews by the GP or pharmacist are arranged if required. Clients are usually visited in the morning with many requiring one or two additional visits throughout the day. This represents a significant use of nursing resources. A blister pack is recommended due to:

  • Poor strength and/or dexterity of fingers limiting ability to open screw-top medication bottles
  • Limited knowledge or understanding of medications
  • Poor medication compliance
  • Cognition or memory issues that impact the client's ability to self-manage medications.

This PhD project involves the development of an Internet of Things device with advanced image processing techniques to monitor the usage of the blister packs at home. A service platform will be developed to support data collection of medication usage, analysis and alarms to prompt actions by the patients and care teams. A codesign approach will be taken with relevant consumer groups in collaboration with our partner Bendigo Health. Trials of the new service platform will be conducted with consumer groups in Regional Victoria and in Sheffield in the UK to test its usability.

Applicants with the following knowledge and/or experience are preferred: Image Processing, Machine Learning, a track record of published (or submitted) research articles.

Project: IntelliParalegal: Leveraging natural language processing techniques to assess immigration asylum seeker applications

Lead Supervisor: Prof Henry Duh

Other Supervisors: Dr Lydia Cui (LTU), Dr Laurence Hirsch (SHU), Dr Abdel-Karim Al-Tamimi (SHU)

Legal Artificial Intelligence (LegalAI) applies artificial intelligence to benefit various tasks in the legal domain. The proposed project will assess the current state of LegalAI and develop new tools which will be of particular use in the area of immigration and asylum seeker applications. In 2021, the UK processed a total of 56,495 asylum applicants with a 63% increase since last year. Out of those applicants, the UK offered asylum to 14,734 people with a success rate of around 26% (National Statistics, 2022). In the same year, the UK granted 205,528 work-related visas. This huge volume of applications demands a challenging amount of resources, which negatively impacts both the effectiveness of the UK legal system and the speed at which these applications are processed. An incorrect decision could result in unjustified removal and expose applicants to the threat of torture or persecution (Gill et al., 2020).LegalAI is a broad term that refers to applying a wide range of natural language processing (NLP) techniques to improve the processes in the legal domain. The focus of this project will be on Legal Judgement Prediction (LJP) and Similar Case Matching (SCM) where good progress has been made in recent years (Zhong et al. 2020). As a direct result of this project, significant benefits to those working in the area of asylum seeker applications should be achievable.Large text databases will be used and advanced natural language processing will be employed to produce effective tools based on recent advances in AI and machine learning. For example, recent research has indicated that text clustering and classification can be achieved via the evolution of human-understandable search queries (Hirsch et al. 2021). Such an approach could be applied to the asylum datasets to improve both LJP and SCM in a transparent manner. The project should produce artefacts that are of genuine use to the legal profession. We, therefore, propose that an agile approach is employed whereby legal experts from SHU and LTU are involved such that they have decisive input into the development of the tools at regular stages.

Project: Data driven design of dual-action biologically active and luminescent transition metal complexes

Lead Supervisor: Dr Peter Barnard

Other Supervisors: Dr Conor Hogan (LTU), Dr Alex Hamilton (SHU), Dr Alex Shenfield (SHU)

Monitoring and understanding biological processes and disease mechanisms through visualisation of cell dynamics is an important biomedical investigative technique. To differentiate cells and sub-cellular organelles selective luminescent probes are required. The use of transition metal complexes as luminescent probes has a number of distinct advantages. The tuneability of the ligand structural and electronic properties allows for families of complexes, with subtly different properties and selectivity to be synthesised. Additionally, the use of biological active transition metals (e.g. Au, Pt and Ru) has the potential for dual-action imagining and therapeutic effects. The photochemical processes of many of these complexes is understood, however, the ability to accurately predict photochemical properties and biological activity and therefore undertake in-silico rapid high-throughput screening of potential novel complexes, prior to focussed synthetic efforts, is currently not possible.

In recent years the application of Machine Learning models to chemical problems has revolutionised predictive capabilities and rational compound design. This project aims to harness the power of these methods to design biologically active transition metal-based imagining agents. By using quantum chemically derived data-driven Machine Learning methods we aim to explore the active chemical-space of transition metal luminophores, allowing for classification of active families of complexes and identification of untapped areas of chemical spaces. The detailed knowledge of chemical-space, and the appropriate quantum/topological descriptors for its analysis, will facilitate rapid high-throughput screening of novel complexes prior to synthesis and biological testing. The proposed project envelopes the entire research production line; from Machine Learning derived in-silico design to synthesis and testing novel complexes for their biologically active luminescent probes properties.

Scholarships for a range of different projects in the same program are also being offered by Sheffield Hallam University; further information on these scholarships is available through Sheffield Hallam .

Benefits of the scholarship
  • a stipend for up to three and a half (3.5) years, with a value of $33,500 per annum (2023 rate)
  • a Research Training Program - Fees Offset scholarship covering tuition fees for up to four (4) years.
  • a travel allowance to assist with travel between Melbourne and Sheffield and personal expenses while resident in the UK
  • an allowance to relocate to Melbourne to commence the degree and publication/thesis allowance or RTP allowance
  • opportunities to work with outstanding researchers at La Trobe and Sheffield Hallam universities, and have access to our suite of professional development programs

In selecting successful applicants, we prioritise applications from candidates who:

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