David Bickel, statistician

Ideal for a fourth-year project or summer internship

THE EDGE. Acquire a statistical bioinformatics skill set by developing novel scientific software in the frontiers of post-genomic biology for high impact on medical science.
THE LAB. Scientific breakthroughs from genome-sequencing projects brought the realization that reliable interpretation of the resulting information makes unprecedented demands for contemporaneous advances in computation and mathematical modeling. As the complexity of genomic data sets drives innovative statistics research, the new Statistical Machine Learning in Functional Genomics (Statomics) Lab of the Ottawa Institute of Systems Biology aims to develop and apply novel methodology and algorithms to solve current problems in analyzing gene-expression, proteomics, metabolomics, SNP, ChIP-chip, and/or clinical data. The lab is presently targeting the inference of degrees of differential gene expression and improvements in the repeatability of microarray results and will attack similar statistics and machine learning challenges of importance to functional genomics.
THE STUDENT. Learn to analyze genomics data with newly created statistical methods. Make breakthrough bioinformatics software accessible worldwide by improving the usability and functionality of the lab’s data analysis code and by adding documentation. Providing scientists with these reliable biostatistical tools can advance medical research by improving the accuracy of conclusions drawn from genomics and clinical data.

Send your cover letter and CV or resume, including your GPA, to dbickel0@uottawa.ca (without the zero).