Promotion in Medizin, Gesundheitswissenschaften und Psychologie

Are you passionate about vision science and innovation in eye care?

This exciting Industry PhD project offers the opportunity to explore personalised approaches to myopia management, in collaboration with Carl Zeiss Pty Ltd, the Australian arm of the globally renowned ZEISS Group.

Project Overview
Myopia is a progressive and irreversible condition that develops during childhood. Orthokeratology is an established method for correcting myopia and slowing its progression. However, treatment outcomes vary widely, likely due to the one-size-fits-all approach to treatment.

This research project will investigate how orthokeratology lens parameters influence corneal shape and optical defocus, with the goal of identifying key factors that enable truly personalised treatment strategies.

Industry Collaboration
Carl Zeiss Pty Ltd brings world-leading expertise in optics and optoelectronics, with a strong footprint in medical technology, vision care, and precision engineering. As an industry PhD candidate, you will work closely with ZEISS professionals and UNSW academics, contributing to impactful research that bridges science and industry.

This project is part of the prestigious National Industry PhD program providing the successful candidate with access to the Campus Plus national network, including a cohort of other National Industry PhD participants. Through this network, they will benefit from tailored professional development opportunities, industry connections and a strong professional community. 

$39,206 per annum (2026 rate) + $15,000 per annum top-up from our Industry Partner + $7000 NIPhD top-up.

https://www.unsw.edu.au/research/hdr/our-projects/optimising-performance-of-orthokeratology 

More than 50% of cancer patients receive radiotherapy (RT) as part of their treatment. Accurate tumour positioning during RT is crucial for effective therapy. However, physiological and involuntary patient movements during dose delivery can lead to suboptimal targeting and unintended high doses to healthy tissues, compromising treatment efficacy and increasing toxicity risks. Over 98% of RT patients worldwide are treated with general-purpose linear accelerators (linacs), which lack real-time target position adaptation.

This project aims to develop and validate a Dynamic Couch system, a moveable patient couch that adjusts in real time during RT to compensate for motion, ensuring precise tumour positioning. This system will enable safer reductions in safety margins, lowering radiation exposure to critical structures and minimizing toxicity. By project completion, a prototype will be ready for clinical trials. 

The Dynamic Couch system will enhance RT precision for >98% of patients, improving tumour control and reducing treatment-related toxicity. 

This project is in collaboration with South Western Sydney Local Health District and is part of the prestigious National Industry PhD program providing the successful candidate with access to the Campus Plus national network, including a cohort of other National Industry PhD participants. Through this network, they will benefit from tailored professional development opportunities, industry connections and a strong professional community.

~$56,206 p.a. tax free. This includes: UNSW scholarship, industry and NIPhD top-ups.

Dr Jonathan Chee [email protected], Institute for Respiratory Health

Kofi Stevens [email protected], Institute for Respiratory Health  

Background  

Mesothelioma is a rare and aggressive cancer with limited treatment options and poor survival outcomes. Although treatments designed to activate the immune system against cancer have demonstrated some clinical benefit, the majority of mesothelioma patients fail to respond. 

Emerging evidence implicates copper as a critical factor in tumour progression. Copper accumulates in mesothelioma tissue, where it promotes tumour growth, immune evasion, and resistance to chemotherapy. Drugs that bind and reduce copper levels (copper chelators) are already approved for non-cancer indications, offering a unique opportunity for drug repurposing. 

This project will explore how manipulating copper availability affects tumour cell behaviour, immune cell function, and overall treatment response. Our goal is to determine whether copper chelation can improve the efficacy of standard therapies and immune-based treatments in mesothelioma. 

Aims 

• Investigate copper levels alters tumour cell behaviour and gene expression, including chemotherapy transporters, MHC-I, and PD-L1.
• Examine how copper affects tumour cell sensitivity to chemotherapy.
• Assess the impact of copper on immune cell function and tumour visibility using in vitro T cell–tumour cell killing assays. 
• Characterise how copper chelation therapy modulates immune cell composition and activity within the tumour microenvironment. 
• Evaluate the in vivo effectiveness of copper chelation therapy, alone or in combination with chemotherapy and/or immunotherapy, and its impact on the tumour microenvironment. 
• Use CRISPR-Cas9 to knock out key copper transporter genes in tumour and immune cells to dissect their role in treatment response. 
• Analyse publicly available datasets to investigate expression of copper-regulatory genes in cancer patients and correlate these with clinical outcomes and treatment responses. 

Outcomes 

Chance to travel to Sydney to work with UNSW collaborators. 

Read more about the project here.

Multiple funding sources from Cancer Council WA, WA Department of Health.

Dr Jonathan Chee [email protected], Institute for Respiratory Health

Dr Nicola Principe [email protected], Institute for Respiratory Health

Project Title 

Immunotherapy, particularly immune checkpoint blockade (ICB), has transformed the treatment of advanced lung cancer and mesothelioma. However, up to 70% of patients do not respond to these therapies, highlighting an urgent need to improve their effectiveness. 

One of the major barriers to successful immunotherapy is the presence of regulatory T cells (Tregs) in the tumour. These cells suppress the immune system and prevent it from attacking the cancer. Recent research suggests that Tregs inside tumours rely heavily on cholesterol and lipid metabolism to maintain their function. Interestingly, clinical studies have shown that patients taking cholesterol-lowering drugs may respond better to ICB, suggesting a potential way to target Tregs and improve outcomes. 

Project Overview 

This project aims to understand how altering lipid metabolism, particularly cholesterol, can reduce Treg-mediated immune suppression and enhance the body’s immune response to cancer. 

Aims  

• Investigate how cholesterol-lowering drugs affect Treg survival, function, and gene expression. 
• Assess how changes in lipid metabolism alter Treg suppression of anti-tumour T cells. 
• Use co-culture assays to test how modifying Treg metabolism impacts immune cell killing of cancer cells. 
• Apply CRISPR-Cas9 to disrupt key metabolic genes in Tregs and explore effects on immune suppression. 
• Evaluate how cholesterol-lowering treatments such as statins influence Treg populations in the tumour microenvironment. 
• Test whether combining metabolic therapies with ICB improves tumour control in mouse models of lung cancer and mesothelioma. 
• Analyse patient data to explore links between cholesterol metabolism genes, Treg signatures, and immunotherapy outcomes. 

Why Join This Project? 

• Work at the intersection of immunology, metabolism, and cancer therapy.
• Gain experience in cutting-edge techniques including CRISPR, flow cytometry, and in vivo tumour models, lipidomics and genomics.
• Contribute to translational research with real clinical relevance.
• Opportunity to collaborate with national experts in tumour immunology and lipid metabolism.

Read more about the project here.

Multiple funding sources from Cancer Council WA, WA Department of Health.

Professor Andreas Wicenec | [email protected]; Director, Data Intensive Astronomy

Dr. Fuling Chen | [email protected]; Research Fellow, Data Intensive Astronomy

Machine learning models, including linear regression, ensemble methods like random forests, and deep neural networks, have transformed medical research by enabling breakthroughs in diagnostics, drug discovery, and personalized treatment. However, these models often function as "black boxes," offering little insight into how they arrive at predictions. This lack of interpretability hinders trust and adoption in healthcare, where understanding the reasoning behind decisions is critical for clinical validation and patient safety.

This PhD project aims to address the urgent need for explainable AI (XAI) in medical research by developing algorithms that combine predictive accuracy with clear, interpretable outputs. Emerging approaches like Kolmogorov-Arnold Networks (KANs) and techniques such as SHAP or LIME provide pathways to make AI decisions transparent. By creating models that explain their processes and results, this research will empower medical professionals to validate AI outputs, uncover biological insights, and ensure ethical, equitable healthcare solutions.

As a PhD candidate, you will design and implement novel XAI models tailored for medical datasets, including epidemiology, genomics, and epigenetics data. Using Python and frameworks like PyTorch or scikit-learn, you will prototype algorithms, test them on real-world medical benchmarks, and evaluate both accuracy and interpretability. You will collaborate with AI experts and clinicians, contributing to publications in top journals and conferences.

Your work will advance the field of medical AI by creating trustworthy, interpretable models that could improve disease detection or treatment planning. We welcome candidates with a background in computer science or related fields, proficiency in programming, and a passion for impactful research.

Read more about the project here.