Spatial Computational Biologist

What you will love about working with us:

At the Wesley Research Institute, you will love being part of a mission-driven team dedicated to improving patient outcomes through impactful clinical research. You will work in a supportive, collaborative environment where your ideas are valued and innovation is encouraged.  If you want to build a rewarding career knowing the work, you contribute makes a real and lasting difference to individuals and communities.

What you will do most days:

1. Research

• Apply advanced analysis techniques to spatial transcriptomics and proteomics datasets to investigate the impact of the tumour microenvironment upon patient treatment outcomes across multiple solid cancer types.
• Develop structured analysis methods aligned with the interests of the QSBC for the publication of high impact research papers.
• Exploration and implementation of state-of-the-art techniques in deep learning, with a focus on spatial foundation models, to drive interpretable discoveries from spatial multiomics datasets.
• Creatively develop tools and packages for the joint analysis of paired spatial multiomics and clinical data, including novel methods for publication.       
• Investigate published and open-source spatial and statistical methods for their utility and application, particularly to drive advanced statistical modelling, interpretation, and translational application.
• Identify gaps in imaging and single cell spatial analysis topics that could lead to the development and implementation of new research techniques.     
• Implement advanced modelling and statistics relevant to categorical and time-to-event data to enable translational discoveries from high-dimensional spatial-omics datasets.  This includes development of novel methods to model tissue architecture, cell-type composition and interactions, and establish statistically robust methods to model clinical outcomes from these measures.
• Exploration of techniques to derive image-based diagnostic predictors.
• Assist with external proposals, funded projects (including industry collaborations), internal discussions, and dissemination of results through internal presentations, journal publications, and conference presentations.

2. Supervision and Researcher Development

• Contribute to supervision of Higher Degree by Research students.
• Contribute to development and upskilling of research group members. 
• Manage research support staff effectively throughout the employee lifecycle in accordance with policy and procedures.
• Working to promptly resolve conflict and grievances when they arise in accordance with policy and procedures.

3. Citizenship and Service     

• Demonstrate citizenship behaviours that align to the WRI values.
• Develop external relationships with industry, government departments, professional bodies and the wider community.
• Perform a range of administrative functions.
• Contribute to activities that benefit the organisational unit, including participation in decision-making and serving on internal committees.
• Any other duties as reasonably directed by your supervisor.

What will position you for success:

• PhD in computational biology, biostatistics, bioinformatics, or a related field, with strong training in computer science and/or advanced statistics.
• Experience with scientific computing in Python and/or R, including libraries such as scikit-learn, PyTorch, lifelines, or advanced statistical packages in R. Experience with or willingness to learn multi-omics analysis frameworks such as SpatialData or SpatialExperiment.
• Interest in developing and applying emerging computational approaches for spatial omics, including the adaptation and training of spatial foundation models.
• Ability to contribute across both advanced method development and routine analytical research tasks.
• Strong motivation to address analytical challenges in spatial multi-omics data, with the goal of producing high-quality scientific outputs such as high-impact publications.
• Excellent communication skills, including the ability to discuss complex analytical challenges with team members, collaborators, and institutional stakeholders.
• Demonstrated ability to work collaboratively in interdisciplinary research teams, contributing proactively to shared solutions and collective goals.
• Ability to critically evaluate and adapt published methods and literature for local development and implementation.
• Experience with reproducible research practices, including coding standards, documentation, and version control (e.g., Git).
• Commitment to research transparency and reproducibility in computational and analytical workflows.