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 daiglelab.org.
Assistantships are available for students interested in pursuing Ph.D. research in bioinformatics and/or computational systems biology. Active research areas in the lab include computational identification of disease biomarkers from high-throughput omics data and machine learning-based inference of gene regulatory networks (GRNs) from single-cell data. We currently have openings in two projects from these areas: (1) biomarker discovery for post-traumatic stress disorder (PTSD) from clinical and molecular data collected from military populations, and (2) application of 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 R and/or Python programming experience. Prior research experience in bioinformatics and/or computational biology is desirable. Details about admission and degree requirements can be found at www.memphis.edu/biology/graduate (Ph.D., Biological Sciences). To ensure full consideration, applications should be completed by February 1. Accepted students will be supported through a graduate assistantship.