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Statistics & Biostatistics Master’s Studentships in Ottawa
Reliable interpretation of genomic information makes unprecedented demands for innovations in statistical methodology and its application to biological systems. This unique opportunity drives research at the Evidence and Likelihood Lab of the Ottawa Institute of Systems Biology (http://www.davidbickel.com). David Bickel seeks new graduate students who will conduct original research involving the creation and evaluation of novel statistical tools for application to the analysis of transcriptomics, proteomics, metabolomics, and/or genome-wide-association data.
Each student will work toward an MSc degree in the Mathematics and Statistics Program at the University of Ottawa. MSc students have the additional option of choosing a Bioinformatics or Biostatistics Specialization. Financial support is available.
Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Strong self motivation and good communication skills are also absolutely necessary. The following qualities are desirable but not required: coursework in bioinformatics, computer science, numerical methods, numerical analysis, software engineering, statistics, and/or biology; familiarly with R, S-PLUS, Stan, JAGS, Mathematica, C, Fortran, and/or LaTeX; experience with UNIX or Linux.
Canadians (by citizenship or permanent residency) are especially encouraged to apply, as are all exceptional students. To be considered, send a PDF CV that has your GPA and contact information of two references to dbickel@uOttawa.ca with a cover letter in the body of the message. Please indicate in the subject line of the message your immigration status (“Canadian citizen,” “Canadian PR,” or “visa”) and, optionally, a specialization (“Bioinformatics” or “Biostatistics”). Only those selected for further consideration will receive a response.
Undergraduate research project or internship
Acquire a statistical bioinformatics skill set by developing novel scientific software in the frontiers of genomics for high impact on medical science. Learn to analyze genomics data with newly created statistical methods. Make new biostatistics software accessible worldwide by improving the usability and functionality of the Statomics 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.
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 Statomics Lab (http://davidbickel.com) 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.
Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Strong self motivation and good communication skills are also absolutely necessary. The following qualities are desirable but not required: coursework in bioinformatics, 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.
To be considered, send a PDF CV that has your GPA and contact information of two references to dbickel@uOttawa.ca with either “research project” or “internship” in the Subject line of the message and with a cover letter in the body of the message. Only those students selected for further consideration will receive a response.
Statistics & biostatistics graduate studentships
Reliable interpretation of genomic information makes unprecedented demands for innovations in statistical methodology and its application to biological systems. This unique opportunity drives research at the Statomics Lab of the Ottawa Institute of Systems Biology (http://davidbickel.com). The Statomics Lab seeks new graduate students who will conduct original research involving the creation and evaluation of novel statistical tools for application to the analysis of transcriptomics, proteomics, metabolomics, and/or genome-wide-association data.
Each student will work toward an MSc or PhD degree in the Mathematics and Statistics Program at the University of Ottawa. MSc students have the additional option of choosing a Bioinformatics or Biostatistics Specialization. Financial support is available.
Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Strong self motivation and good communication skills are also absolutely necessary. The following qualities are desirable but not required: coursework in bioinformatics, 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.
Canadians (by citizenship or permanent residency) are especially encouraged to apply, as are all exceptional students. To be considered, send a PDF CV that has your GPA and contact information of two references to dbickel@uOttawa.ca with either “MSc” or “PhD” and any specialization in the Subject line of the message and with a cover letter in the body of the message. Only those selected for further consideration will receive a response.
Statistics & biostatistics graduate student stipends
Reliable interpretation of genomic information makes unprecedented demands for innovations in statistical methodology and its application to biological systems. This unique opportunity drives research at the Statomics Lab of the Ottawa Institute of Systems Biology (http://www.davidbickel.com/). The Statomics Lab seeks students who will conduct original research involving the application of novel statistical methods to the analysis transcriptomics, proteomics, metabolomics, and/or genome-wide-association data while earning a graduate degree in Mathematics and Statistics. The page https://davidbickel.com/career/ has information on careers in statistics and biostatistics.
Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Strong self motivation, 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.
To be considered, send a PDF CV that has your GPA and contact information of two references to dbickel@uOttawa.ca with the degree sought (MSc or PhD) in the Subject line of the message and with a cover letter in the body of the message. Only those students selected for further consideration will receive a response.
Bioinformatics / biostatistics master’s studentships
Reliable interpretation of genomic and neurological information makes unprecedented demands for innovations in statistical methodology and its application to biological systems. This unique opportunity drives research at the Statomics Lab of the Ottawa Institute of Systems Biology (http://www.statomics.com). The Statomics Lab seeks new graduate students who will conduct original research involving the application of novel statistical tools to the analysis of transcriptomics, proteomics, metabolomics, genome-wide-association data, and/or neuroscience data.
Each student will work toward an MSc degree in either the Biochemistry Program or the Mathematics Program at the University of Ottawa. Both programs provide the option of a Bioinformatics Specialization, and the Mathematics Program also offers a Biostatistics Specialization. Financial support is available.
Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Strong self motivation and good communication skills are also absolutely necessary. The following qualities are desirable but not required: coursework in bioinformatics, 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.
To be considered, send a PDF CV that has your GPA and contact information of two references to dbickel@uOttawa.ca with either “Biochemistry” or “Mathematics” and any specialization in the Subject line of the message and with a cover letter in the body of the message. Only those students selected for further consideration will receive a response.
Research at the Statomics Lab
Overview
At the Statomics Lab, we discover ways to assess complex information relevant to health care, renewable energy, and other applications in the post-genomic era. Improved statistical methods of weighing evidence enable more reliable interpretations of both case-control measurements of genomes and experimental measurements of transcript, protein, and metabolite levels in the cell. A more thorough understanding of these data impacts biomedicine and biotechnology, targeting higher-quality health care and sustainable energy availability.
David Bickel and the trainees in the Statomics Lab are improving statistical methods of weighing evidence to enable more reliable interpretations of both (1) experimental measurements of transcript, protein, and metabolite levels in the cell and (2) case-control measurements of genomes.
Statistical systems biology
In the first component of the research program, the lab is developing statistical methods for the analysis of gene expression microarray data and other functional genomics data. The methods include the creation and testing of new ways to estimate levels of microarray gene expression. For example, this involves work on analogous methods for the case of unpaired data such as that of proteomics and metabolomics platforms and of single-channel microarrays and reliable estimation of the fold change of each gene. Since the emerging field of lipidomics has a need for such methods of data analysis, David Bickel is a mentor in the CIHR Training Program in Neurodegenerative Lipidomics.
Inferring genome-wide associations
For the second component of this research program in high-dimensional statistics, the lab is extending similar methods developed for gene expression data to genome-wide association (GWA) studies, as follows. We are developing and comparing statistical methods of estimating odds ratios while considering concerns about multiple comparisons. In particular, we are inventing shrinkage estimates in the presence of multiple comparisons. We are also creating methods of reliably approximating probabilities of association in order to obtain better point and interval estimates of the effect sizes.
More information
For details on the research summarized above, see the lab’s publications and preprints.
Computational biostatistics student stipends
Reliable interpretation of genomic information makes unprecedented demands for innovations in statistical methodology and its application to biological systems. This unique opportunity drives research at the Statomics Lab of the Ottawa Institute of Systems Biology (http://www.statomics.com). The Statomics Lab seeks students who will conduct original research involving the application of novel statistical methods to the analysis transcriptomics, proteomics, metabolomics, and/or genome-wide-association data while earning a graduate degree in Mathematics and Statistics. For information on careers in statistics, see http://tiny.cc/Rqvnf and http://amstat.org/careers/.
Intellectual curiosity and high mathematical aptitude are essential, as is the ability to quickly code and debug computer programs. Strong self motivation, 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.
To be considered, send a PDF CV that has your GPA and contact information of two references to dbickel@uOttawa.ca with the degree sought (MSc or PhD) in the Subject line of the message and with a cover letter in the body of the message. Only those students selected for further consideration will receive a response.
Bioinformatics graduate program
Ottawa-Carleton MSc/MCI Program in Bioinformatics
David Bickel is currently accepting new students.
For more information on the field of bioinformatics, see the slides from the First Canadian Workshop on Statistical Genomics, the links provided by Georgia Tech, and the jobs posted at the Canadian Bioinformatics Workshops.
Undergraduate research opportunity
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).
David Bickel, statistician 
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