(Level A) $75,888 to $102,040 per annum plus an employer contribution of 17% superannuation applies.
Fixed term, full time position available for 24 months.
Advance cancer diagnosis by analysing immune system responses and treatment efficacy using spatial and single-cell computational immunogenomics.
The position is for a postdoctoral fellow in spatial and single-cell computational immunogenomics for cancer diagnosis. This position will advance cancer diagnosis by analysing immune responses and treatment efficacy through data-driven research and a multi-omic approach. Exploring the dynamics of circulating immune cells in blood reveals crucial insights into the immune system's response, its efficacy against metastatic cancer, and its reaction to treatments. Profiling peripheral blood immune single cells and circulating cytokines, we uncovered patterns of immune cell communication and composition linked to metastatic progression (Mangiola et al. 2024).
This exciting position aims to expand this research to study the local tumour microenvironment and apply this approach to identify individual immune characteristics influencing immunotherapy success. We aim to employ a multiomic approach, analysing extensive patient data across all stages of disease progression. This research will utilise state-of-the-art facilities, including 10x Xenium, 10x CITE-seq, Milliplex and proteomics. We are searching for a dynamic individual for the Computational Biologist/Bioinformatician/Biostatistician role. Managing personal research funds is a possibility depending on the appointment level and the candidate's experience.
Key responsibilities: Develop and apply advanced AI large-language models to analyse large-scale single-cell datasets; Contribute to the expansion and refinement of our large-scale single-cell database; and collaborate with a multidisciplinary team to advance the field of precision oncology.
To be successful you will need:
Explicitly address each selection criteria
PhD in Computational Biology, Bioinformatics, Computer Science, or a closely related field with a strong focus on machine learning and deep learning applications.
Demonstrable experience in analysing large-scale single-cell genomic data.
Proficiency in programming languages commonly used in computational biology and data science, such as Python and R, with the ability to handle complex data analysis tasks. Please provide publicly available examples.
A strong record of research, evidenced by publications in peer-reviewed journals or presentations at significant conferences, particularly in areas related to AI, language modelling, computational biology, bioinformatics, or immunogenomics.
Learn more at: adelaide.edu.au/jobs
Applications close 11:55pm, 18 July 2024.
The University reserves the right to close this advertisement before the closing date if a suitable candidate is identified.
For further information
For a confidential discussion regarding this position, contact:
Stefano Mangiola Group Leader, SAiGENCI E: stefano.mangiola@adelaide.edu.au
