• The Australian Centre for Artificial Intelligence in Medical Innovation (ACAMI) is jointly funded by the Victorian State Government and La Trobe University. ACAMI is the world’s first university innovation centre specialising in the application of AI to accelerate the discovery and development of immunotherapies, vaccines and medical innovation, focusing on collaborative research, workforce development and clinical research. It is powered by Australia's first NVIDIA DGX H200 computer. 

Benefits of the scholarship:

• be a part of the world's first university innovation centre specialising in the application of artificial intelligence to accelerate the discovery and development of medical innovation.
• Access to an industry leading platform for AI development.
• Undertake highly novel cross-collaborative research with La Trobe’s outstanding researchers
• Opportunity to publish findings in high-impact peer-reviewed journals

AVAILABLE PROJECTS

There are three scholarships available with projects outlined below, competitively awarded with selection based on academic merit and suitability to the project. Please contact the lead supervisor for more information about your preferred project.

Project: Hypertension-specific neoantigen identification using B cell receptor molecular modelling  

Lead supervisor: Dr Hericka Bruna Figueiredo Galvao

As an applicant, you will have a background in computer science, ideally with a specialisation/major in machine learning/artificial intelligence and biology. Prior knowledge/experience with immunology and structural modelling/docking is advantageous though not required. In addition, you will acquire the necessary medical/biological knowledge through a team of researchers who will support you through your project.

In Australia, 1 in 3 adults have hypertension however the cause remains unknown in ~90% of cases. Scientists have known since the 70s that hypertensive adults show increased circulating antibody levels which are exclusively produced by B lymphocytes/cells. Most antibodies are highly target-specific and are more frequently expressed during an immune response. The Hypertension and Immunobiology Research Division of the Centre for Cardiovascular Biology and Disease Research at La Trobe recently acquired amino acid antibody sequences from hypertensive mice which will be used for a target-identification campaign. This project involves modelling the 3-dimensional structure of these sequences and using a combined score from multiple docking algorithms to identify lead candidates for the development of novel therapies.

Additional Eligibility:

• have an undergraduate degree in computer science with a major in machine learning/artificial intelligence
• have an undergraduate degree assessed at a H1 average (>80) equivalent
• have experience in structural modelling/docking campaigns

How to apply:

• Email your undergraduate academic transcript with a cover letter to h.figueiredogalvao@latrobe.edu.au
• Shortlisted applicants will be required to attend an interview with the supervisory panel and if successful, will be required to complete a formal honours project application.

Project: A simplified, artificial intelligence-based assessment for histology slides to predict outcomes for colon cancer patients

Lead supervisor: Amr Allam

Location: ACAMI at La Trobe, and Olivia Newton-John Cancer Research Institute (ONJCRI).

The honours student will play a key role in advancing the project by contributing to the development of an automated analysis pipeline. This includes writing code to analyze histological data, integrating algorithms, and testing solutions on a subset of Stage II/III colon cancer samples.

In this project, we aim to establish an artificial intelligence (AI)-based assessment model for tumour infiltrating lymphocytes, which will allow a better predication for colorectal cancer patients outcomes compared to traditional systems. To do this, HALO artificial intelligence-based algorithms will be trained on tissue microarray images (TMAs) to not only determine TIL density but also recognize the spatiotemporal characteristics of TILs in colorectal cancers.

In addition, we aim to establish an artificial intelligence (AI)-based assessment model for tumour infiltrating lymphocytes, which will allow a better predication for colorectal cancer patients outcomes compared to traditional systems. To do this, HALO artificial intelligence-based algorithms will be trained on tissue microarray images (TMAs) to not only determine TIL density but also recognize the spatiotemporal characteristics of TILs in colorectal cancers. In addition, we will use VECTRA multiplex imaging system enables the simultaneous identification of different (immune) cell subtypes through analysis of 7 cell type-specific markers on the same tissue section. Thus, we have combined results from the VECRTA analysis of CRC tissue sections with HALO AI. Furthermore, preliminary results showed that collagen deposition and organization refines our stratification model, hence we will use the novel emerging NanoMslide technology to assess collagen deposition and cross-linking and incorporate with TILs infiltration assessment as an added parameter.

What You'll Gain:

• Hands on experience at the intersection of AI, histopathology, and cancer research.
• Opportunity to collaborate with leading researchers in oncology and pathology.
• Hands on experience on histology techniques and understand tissue processing for clinical and fundamental research. 
• Opportunity to contribute to a cutting-edge project with clinical translation potential.
• Exposure to industry and clinical research. 
• Access to state-art-facilities at ACAMI and ONJCRI 

Additional Eligibility:

• have experience in coding (R programming language preferred).
• have basic understanding of cancer biology and immunology (preferred but not essential).
• interested in artificial intelligence and machine learning applications in healthcare.

How to apply:

• Email your undergraduate academic transcript and updated CV to: amr.allam@onjcri.org.au
• Shortlisted applicants will be required to attend an interview with the supervisory panel