statomics.com

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 Inference and Computation in Genomics (Statomics) Lab of the Ottawa Institute of Systems Biology seeks MSc and PhD students who will develop and apply novel methodology and algorithms to solve current problems in analyzing gene-expression, proteomics, metabolomics, SNP, ChIP-chip, and/or phenotypic data. The lab is presently targeting inference in genome-wide association studies, estimation of levels of gene expression, and improvements in the repeatability of microarray results and will attack similar statistical and computational challenges of importance to genetics and functional genomics.

The OISB provides a highly collaborative research environment with ample opportunities to interact with leading experimental and computational biologists. In addition, each student’s background will complement that of any students and any postdoctoral researchers to be recruited to the Statomics Lab from the statistics, bioinformatics, and computer science communities, creating interdisciplinary synergism for making unique contributions to science.

Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Canadian citizenship or permanent resident status, strong initiative, good communication skills, and a degree in bioinformatics, computer science, mathematics, physics, statistics, any field of engineering, or an equally quantitative field are also absolutely necessary. The following qualities are desirable but not required: coursework in computer science, numerical methods, numerical analysis, software engineering, statistics, and/or biology; familiarly with BUGS, R, S-PLUS, C, Fortran, and/or LaTeX; experience with UNIX or Linux.

Send a PDF CV that has your GPA and contact information of two references to dbickel0@uottawa.ca (without the zero) with “statistical bioinformatics graduate student” in the Subject line of the message and with your preferred graduate program (Biochemistry, Mathematics & Statistics, or Computer Science) and the degree sought (MSc or PhD) in the message body. Only those students selected for further consideration will receive a response.

Paul Poncet’s modeest package implements the half-range mode, the half-sample mode, and the mode-based skewness of D. R. Bickel, “Robust estimators of the mode and skewness of continuous data,” Computational Statistics and Data Analysis 39, 153-163 (2002).

More mode estimation software

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).