A postdoctoral fellow position is available within the lab of Dr Jason Wong to work on projects relating to the study of mechanisms of mutation formation in cancer and the impact of these mutations on cellular function. These projects build on recent research from the team described in Nature (https://doi.org/10.1038/nature17437). The successful applicant will carry out computational and bioinformatics data analyses, write research manuscripts and co-supervise research students. There will also be opportunities to undertake experimental research to generate data and test hypotheses, and therefore researchers with experience in laboratory-based research will be highly regarded. The position will be based within the Lowy Cancer Research Centre in the Bioinformatics and Integrative Genomics team led by Dr Jason Wong.
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We are seeking a bioinformatician to join the bioinformatics group of the Centre for Cancer Biology (CCB). This position, available for two years in the first instance, will be primarily associated with the Gene Regulation Laboratory (Prof. Greg Goodall) and the new ACRF Cancer Discovery Accelerator of the CCB. Specific interests of the Gene Regulation Laboratory include the study of the mechanisms and functions of transcriptional and post-transcriptional regulation of gene expression, especially in relation to the epithelial-mesenchymal transition and in cancer metastasis (see http://www.centreforcancerbiology.org.au/research/laboratories/gene-regulation-section/ for further information).
The CCB’s bioinformatics group, headed by Dr. Andreas Schreiber, consists of around 10 bioinformaticians and is located in the ACRF Cancer Genomics Facility. The group coordinates and carries out the computational analysis component of the research program of the CCB, performs bioinformatic developmental work within for the Genome Facility and it is also closely associated with the NGS diagnostic service of SA Pathology. The group specializes in genome analysis, supporting the discovery of disease causing mutations, analysis of changes in expression and regulatory networks during the development of cancer and development of software aiding the transition from biomedical research to personalized genomics.
The successful applicant will be proficient in interpreting large, complex and diverse next-generation sequencing datasets. Previous experience with working with long reads is an advantage, as is a strong statistical background. The applicant will use both available computational tools as well as develop their own. Previous experience in coding in one or more relevant languages (e.g. Perl, Python, Java, shell scripting) is essential, as is familiarity with Unix/Linux. We will consider applications from candidates with a PhD or substantial experience in bioinformatics, statistical, mathematical & computational or related sciences. Previous experience in molecular biology, genetics and related areas is an advantage but not essential.
Closing date for applications is Dec 12, 2016. For further information, and the application form, see "Research Associate: Bioinformatics (Level ARAS)" (Job Ref - HSC_0282_R_E) at