Supervisor: Dr. Bernie Daigle, Jr., Assistant Professor, Departments of Biological Sciences and Computer Science
Experimental biologists are generating data at an unprecedented rate. Unfortunately, biological insight has not kept pace with this deluge of data. The goal of my lab is to improve the inference of biological meaning from the wealth of experimental data collected from single cells to whole organisms. To do so, we develop sophisticated statistical and computational tools that enable integrated analyses of noisy, heterogeneous datasets.
Assistantships are available for students interested in pursuing a Master's or Ph.D. in bioinformatics and/or computational biology. In the first area, active research projects in my lab involve mining publicly available biological datasets to facilitate the characterization and classification of human disease. Specifically, we are interested in developing more effective supervised and unsupervised machine learning approaches for high-throughput data, using models such as Bayesian networks and artificial neural networks. In the second area, our research involves developing computational methods for inferring the underlying structure and behavior of biological systems. Recent work by our lab in this area includes combining stochastic simulation and optimization techniques to characterize promoter architecture from single-cell gene expression data. More information can be found at http://daiglelab.org.
The successful candidate should be highly motivated and have some computer programming experience (R, MATLAB, Perl, Python, C, or C++). Prior research experience in bioinformatics and/or computational biology is desirable. Details about admission and degree requirements can be found at http://www.memphis.edu/bioinformatics/requirements (MS, Bioinformatics) and http://www.memphis.edu/biology/graduate (MS/PhD, Biological Sciences). Applicants must apply to both The University of Memphis Graduate School and the corresponding graduate program. To ensure full consideration, applications should be completed by February 15. Accepted students will be supported through a graduate assistantship.
If interested, please contact Dr. Daigle (email@example.com) for further information.