Computational approaches to malaria parasite antigen diversity
Duke Global Health Institute

The Taylor lab (sites.duke.edu/taylorlab) is recruiting an exceptional post-doctoral scientist to work on the transmission of malaria parasites between hosts. The candidate will join an existing team at Duke, Johns Hopkins, Moi University, Kemri-Wellcome Trust Research Programme, and the field site in Kenya, and engage with investigators at all sites across various disciplines. The project will focus on conceptualizing, innovating, and implementing data-driven approaches to malaria vaccine design based on innovative, longitudinal data sources describing complex parasite genetics in both human and mosquito infections as well as human immune responses.

Candidates from statistics, bioinformatics, epidemiology, molecular biology, and immunology will all be considered and have valuable perspectives to add to this work. Irrespective of scientific background, the candidate can expect to join a vibrant academic community, develop essential scientific and professional skills, and conduct translational malaria research with explicit impact. The primary work site is Duke University, with the potential for significant time working with partners at the field site in Kenya.

One position is available immediately as Postdoctoral Fellow. This position is based in Durham, NC.

The post-doc will lead the project activities related to this work, including refining analytic approaches, conceptualizing important research questions, implementing data analysis best practices, analyzing data, communicating research findings with project partners, engaging with field partners, and drafting manuscripts. S/he will work closely with Drs. Steve Taylor (Duke), Wendy O’Meara (Duke), Christine Markwalter (Duke), Amy Wesolowski (Johns Hopkins), Francis Ndungu (KWTRP) and Andrew Obala (Moi), as well as the team of research staff in Eldoret, Kenya.

A doctoral degree in a relevant field is required, preferably a quantitative field such as bioinformatics, statistics, computer science or engineering. The ideal candidate will have experience in high-dimensional data analysis, genome-scale analysis, translational research, and molecular epidemiology. Good communication skills and teamwork are essential, as is advanced ability with programming