Data Scientist in Proteomics

Posted 07 May 2019

Children's Medical Research Institute

Australia (Bioinformatician)

A data scientist position is available in the ProCan Cancer Data Science Group, led by Dr. Qing Zhong. ProCan (the ACRF International Centre for the Proteome of Human Cancer) is a world-first initiative developed and launched in September 2016 by Professors Phil Robinson and Roger Reddel, and established with a $10 million grant from the Australian Cancer Research Foundation (ACRF). Equipped with six SCIEX mass spectrometers and a super computer (800TB / 480 cores), ProCan processes tumour samples through a proteomic method, SWATH-MS, which allows fast mass spectrometric conversion of small amounts of tissue (biopsy level) into a single, permanent digital file representing the quantitative proteome of the sample. One of the goals of ProCan is to measure thousands of proteins in about 70,000 cancers of all types with known treatment outcome and correlate tumour proteotypes with clinical phenotypes. The Cancer Data Science Group aims to develop novel computational tools and sophisticated machine learning algorithms to achieve this goal. Other major focuses of the group are 1) big proteogenomic data mining and management, 2) the genome-proteome association analysis and multi-omic data integration for studying cancer, 3) development of advanced statistical tools to account for batch effects caused by large-scale, high throughput proteomics, and 4) implementation of big data-driven, evidence-based computational tools to achieve predictive, preventive, personalized medicine.

We invite applications from PhD scientists to join our group. Applicants should have a high degree of motivation in academic research, an excellent record of scientific accomplishments with publications in peer-reviewed journals, and the ability to work independently with outstanding communication and writing skills. Candidates should have a strong background in bioinformatics, biostatistics or machine learning. Strong programming skills in R and Python are required. Previous experience in proteomics is essential. You will be responsible for conducting biostatistical analysis, performing scientific data visualization, developing solutions to proteomics research and cancer biomarker discovery.

How to Apply