Postdoctoral Position – University of Maryland, School of Medicine
Posted: 12/11/2022 (Employment)
The parasitic infectious disease group of Dr. Joana C Silva, Professor at the Institute for Genome Sciences (IGS, http://www.igs.umaryland.edu ) seeks applicants for one (possibly two) postdoctoral scholar positions. IGS at the University of Maryland, School of Medicine is an interdisciplinary, multi-departmental team of collaborative investigators with a broad research program related to the basic and translational sciences, genomics, epigenetics, and bioinformatics. The impact of the members of IGS on the genomics field has been substantial, with more than 2000 publications during the past 26 years, which have been cited more than 300,000 times, making IGS one of the most productive institutions in the region.
Qualified candidates will be enthusiastic, highly motivated and interested in studying the evolution of malaria parasites, with emphasis on Plasmodium falciparum, and/or host-parasite interactions. Available research topics for this position are varied and include 1) parasite genetic variation and its impact on vaccine escape; 2) evolutionary (distant past) or historical (recent past) P. falciparum demography; 3) host immune responses to parasite co-infection.
This interdisciplinary position benefits from our close interactions with colleagues from the Malaria Research Program ( https://www.medschool.umaryland.edu/malaria/ ) and the Center for Vaccine Development and Global Health ( https://www.medschool.umaryland.edu/CVD/ ), with state-of-the-art programs and resources in malaria, immunology and vaccinology research. This position offers opportunities to address important questions in the malaria, with first access to large novel data sets from various genomic platforms. We have a strong preference for computational biologists/statistician with a deep appreciation of biological phenomena or equally, microbiologists/immunologists with a solid background in computational analysis of omics data. Projects will apply computational, statistical and bioinformatics approaches to integrate multi-omics’ datasets such as genome sequences, bulk RNA-seq, single-cell RNA-seq, cell surface marker information, T/B cell receptor sequences and HLA genotypes to characterize parasite evolution and host-parasite interactions.