Job: Post-Doctoral Scientist, Oncology Bioinformatics, Melanoma Institute Australia, University of Sydney
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2.2 years ago by
james.wilmott0 wrote:
  • World's leading melanoma research & treatment centre
    • Support provided to develop specialist knowledge
    • Located at RPAH, Camperdown, Sydney, Australia

As the world's leading melanoma research and treatment centre, research is what distinguishes Melanoma Institute Australia (MIA) from a regular oncology clinic. Our Melanoma Research Database is the largest in the world, over 40,000 patient records dating back to 1957. Since that time, the Institute has expanded its research assets to include a Biospecimen Bank, genome sequencing projects, as well as data gathered from extensive clinical trials. These assets are made available to researchers around the world to gain a better understanding of the causes, development, diagnosis and treatment of melanoma.

The group has undertaken genomic sequencing projects within leading global initiatives that include TCGA (Cell, 2015), ICGC and PANCAN, as well as conducted its own Australian Melanoma Genome Project, which comprises the world's largest series of melanoma whole genome sequencing data. We have also undertaken whole genome sequencing of a large series of melanomas from patients receiving the new wave of effective targeted and immune systemic therapies. Many of these samples have matching transcriptomic, drug response, and clinical outcome data, which we are interrogating for any biology and biomarkers that can aid in improving personalised medicine.

The Melanoma Pathology Translational Research Group works with our oncology and functional biology groups on translation research projects that discover new drug targets and identify biomarkers of response and resistance in human clinical trials. There is an exciting opportunity for a talented and motivated bioinformatician or biostatistician, eager to bring high quality genomic and clinical data together in new ways. The post holder will focus on development and application of approaches combining the aforementioned multi-omic data sets with phenotypic and drug response data to reveal actionable insights surrounding complex biological problems.

Encouragement and support will be provided to help the individual develop specialist knowledge and skill sets.


Work closely with bioscience and translational science teams to propose appropriate bioinformatic and statistical approaches to their scientific and technical challenges. Focus application of these approaches on immuno-oncology projects. Design and apply innovative computational/statistical algorithms and visualizations to: Generate actionable biological insight from genomic data.

Discover and develop new molecular target, mechanism and biomarker hypotheses for drug projects.

Link multi-omic data sets from patients and in vitro/vivo models.

Integrate and interpret internally generated and public datasets.

Find new ways of interpreting, modelling or finding meaningful patterns in complex data.

Proactively engage in knowledge sharing and peer support, including training our bench science community, to build expertise in the tools critical to Oncology Bioinformatics.



Relevant PhD (or equivalent graduate degree plus experience), plus proven post-doctoral or applied experience, combining technical expertise in either: Genetics/genomics, oncology/immunology and computational biology.

Proficient skills and track record in the generating of high impact publications.


Understanding of the biological systems and signalling involved in human disease. Programming in a Unix and Windows environment. R programming expertise (inc. use of Bioconductor and Shiny). Skilled in effective communication of complex data to a non-expert. Valuable contributions to scientific projects recognized through peer reviewed publication. Either: Deep understanding of the molecular drivers of immunology and/or cancer; plus proficiency analyzing and interpreting data from multiple 'omic platforms (NGS sequencing, transcriptomic, proteomic etc.). Expertise applying mathematical approaches to identify and interpret associations in diverse molecular and phenotypic data; knowledge of large-scale machine learning techniques. Desirable:

Well networked within external bioinformatics and oncology communities. A thorough understanding of the contribution of bioinformatics to drug discovery. Effective contributing to collaborative projects involving cross-disciplinary and global teams. Experience mentoring and supervising PhD students. Expertise in genetic (DNA sequence, NGS) data interpretation. Python/Perl programming expertise. SQL/Database management expertise.

If you are keen to pursue this opportunity, please email .

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