Postdoctoral Associate - Urban Evolution

Description We are seeking a postdoctoral associate to join an NSF-funded project on urban evolution, led by Professors Brad Cosentino and James Gibbs. This project aims to investigate the evolutionary mechanisms that maintain urban-rural clines in phenotypic traits, with coat color in eastern gray squirrels as the model system. The position is fully remote for six months and will focus on writing two manuscripts based on existing data. The first manuscript will explore how non-adaptive evolutionary processes influence the strength of urban-rural clines in squirrel melanism across more than three dozen cities. The postdoc will use camera trap and citizen science observations to investigate how forest fragmentation and barriers to gene flow shape these clines. The topic for the second manuscript will be determined based on the candidate’s interests and data availability. Potential areas of inquiry for the second paper include: Effects of urbanization and coat color on antipredator behavior (e.g., flight initiation distance trials) Comparisons of activity patterns between squirrel color morphs and carnivore predators Comparisons of heavy metal concentrations between color morphs Drivers of intracity variation in coat color prevalence This is a full-time, remote position with a 6-month appointment. There is the possibility of extension, contingent on funding availability. We will also consider part-time appointments for candidates who have other part-time commitments. Compensation is $35,000 for six months, with benefits. Questions can be addressed to Brad Cosentino (cosentino@hws.edu). Review of applications begins immediately and will be on a rolling basis. Qualifications Required qualifications: PhD in evolutionary ecology or related fields Strong track record of publishing scientific research Familiarity with urban evolutionary biology and wildlife biology. Strong ability to work independently, take initiative in moving a research project forward, and effectively manage time to meet deadlines in a remote environment Desirable qualifications: Strong data management, analysis, and coding skills in R Experience working with hierarchical models of wildlife abundance/occupancy