Two Post Doctoral Positions in Systems Biology are currently available at the University of Alberta under the supervision of Dr. Ben-Zvi.  Please see the project descriptions and contact information below.

1. Development of nanocrystalline cellulose based hydrogels for controlled delivery of therapeutics relevant to bone regeneration: controlled delivery strategies, and cellular activity.

Role of Post Doctoral Fellow: The identification of gene networks crucial to regulating the cellular responses to the delivery of therapeutics via nanostructured materials.

Scope: The successful candidate will be expected to design an experiment that will capture the key dynamics (i.e., time-dependent changes) in gene expression levels in response to the introduction of bioactive molecules into the cells.  The candidate will be responsible for experimental design, data collection and analysis, as well as nonlinear regression, and model validation and statistical inference. The candidate will make use of microarray slides to quantify the expression levels of various transcription factors at different points in time.  The data from the microarray experiments will be used to develop a mathematical model (based on differential-algebraic equation systems) that will describe gene dynamics. 

In addition, the successful applicant will be responsible for scholarly publication of experimental and theoretical results.

Qualifications:  The ideal candidate will have experience with the operation of microarray slides and genomics data.  A candidate with strong biochemistry background will be preferred.  The candidate must be mathematically inclined and willing to learn system biology concepts as necessary.  Finally the candidate should have a Ph.D. in Engineering, Biochemistry, Medicine or a related field.

2. Application of fault detection and diagnosis techniques to multivariate metabolomics data including the use of NMR spectroscopy for diagnosis of Streptococcus pneumoniae infection.

Role of Post Doctoral Fellow: The development of a robust data analysis and diagnosis technique that will make use of NMR spectroscopy as well as a-priori information to classify individuals as healthy or not.  Also, the development of a time-dependent model which will allow the progression or intensity of a condition to be assessed. 

Scope: The successful candidate will make use of fault detection and classification techniques including PCA, PLS-DA, and SVM to correctly classify multivariate metaboloimics data.  The candidate will be expected to improve existing algorithms for fault detection or devise new algorithms as necessary.  In particular, the candidate is expected to develop algorithms for time-dependant data, as well as the development of insight into the likely biochemical pathways associated with specific metabolites.  Also, the candidate is expected to develop techniques for visualization and quantification of large data sets.  The successful candidate will be responsible for scholarly dissemination of results. 

Qualifications:  The ideal candidate will have experience with bioinformatics and fault detection and diagnosis tools.  A candidate with strong biochemistry background will be preferred.  The candidate must be mathematically inclined and willing to learn system biology concepts as necessary.  Finally the candidate should have a Ph.D. in Engineering, Biochemistry, Medicine or a related field.