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. More information on the lab can be found at http://daiglelab.org.
Assistantships are available for students interested in pursuing a Ph.D. in computational systems biology. Our research in this area involves developing and applying computational methods for inferring gene regulatory networks (GRNs) from single-cell gene expression data. Currently, we have openings on a project applying deep learning techniques to rapidly and accurately infer GRNs from single-cell RNA-sequencing data.
The successful candidate should be highly motivated and have some Python programming experience. 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/biology/graduate (PhD, Biological Sciences). Applicants must apply to both The University of Memphis Graduate School and the Biological Sciences graduate program. To ensure full consideration, applications should be completed by February 1. Accepted students will be supported through a graduate assistantship.
If interested, please first email Dr. Daigle (email@example.com) your CV and a concise statement describing your interest in the position, previous research experience, and relevant coursework.