UNC
Overview
The Liu Lab at the University of North Carolina at Chapel Hill is seeking a highly motivated Postdoctoral Research Associate to investigate the evolutionary mechanisms that shape the pathogenicity and drug resistance of Mycobacterium tuberculosis (Mtb) and nontuberculous mycobacteria (NTM). This position will focus on identifying the genetic determinants that drive bacterial adaptation to the host environment and contribute to antimicrobial resistance and persistence during infection. Responsibilities
Leverage large-scale longitudinal clinical cohorts and bacterial isolate collections obtained through collaborations. Develop and apply novel population-genomic and statistical frameworks to detect non-canonical forms of natural selection, adaptive convergence, and bacterial traits associated with transmission, virulence, and treatment outcomes. Integrate multi-dimensional datasets including genomic, transcriptomic, phenotypic, and clinical metadata to elucidate how bacterial evolution influences disease progression and therapeutic response. Collaborate closely with computational biologists, microbiologists, and clinicians, and contribute to the design of follow-up functional validation experiments.
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The Liu Lab at the University of North Carolina at Chapel Hill is seeking a highly motivated Postdoctoral Research Associate to investigate the evolutionary mechanisms that shape the pathogenicity and drug resistance of Mycobacterium tuberculosis (Mtb) and nontuberculous mycobacteria (NTM). This position will focus on identifying the genetic determinants that drive bacterial adaptation to the host environment and contribute to antimicrobial resistance and persistence during infection. Responsibilities
Leverage large-scale longitudinal clinical cohorts and bacterial isolate collections obtained through collaborations. Develop and apply novel population-genomic and statistical frameworks to detect non-canonical forms of natural selection, adaptive convergence, and bacterial traits associated with transmission, virulence, and treatment outcomes. Integrate multi-dimensional datasets including genomic, transcriptomic, phenotypic, and clinical metadata to elucidate how bacterial evolution influences disease progression and therapeutic response. Collaborate closely with computational biologists, microbiologists, and clinicians, and contribute to the design of follow-up functional validation experiments.
#J-18808-Ljbffr