As part of a new, collaborative NSF-funded research and training program in the genomic ecology of coastal organisms, the University of New Hampshire is seeking three new PhD students who will study genome-phenome relationships in the wild. Students will conduct both field work and genomic analyses towards understanding adaptation and fitness associations in tidal marsh sparrows. Field work during summer months may involve supervising field crews in tidal marshes across the northeastern US, from Maine to Virginia. Genomic analyses may include whole-genome and exome sequencing, candidate gene sequencing, gene expression analyses, meta-barcoding, and microbiome characterization. All students will have the opportunity to work in collaboration with a diverse team of investigators, graduate students, and undergraduate students at both institutions.
Students will be enrolled in the Natural Resources and Earth Systems Science (NRESS) program (https://www.unh.edu/nressphd)at the University of New Hampshire and will be advised by Dr. Adrienne Kovach (http://www.kovachlab.com). Students will conduct research in the following three focus areas: 1) Genetic underpinnings of nesting adaptations and links between nesting traits, including the timing of nest initiation, candidate genes for circadian rhythms, and reproductive fitness across sparrow species; 2) Parallel evolution – this student will investigate broadscale genotype-phenotype and genotype-environment relationships in relation to tidal marsh adaptations for six sparrow species sampled across an environmental and geographic gradient; and 3) Diet and eco-evolutionary feedbacks – this student will study variation in sparrow diets using meta-barcoding of fecal samples and the linkages with fitness and the trophic impacts on the marsh; this student will also work closely with Drs. Serita Frey (UNH) and Mike Kinnison (UMaine).
Qualifications: Successful candidates must have a strong background in ecology and/or genomics. Preferred candidates will have demonstrated experience with field work, in particular, bird mist-netting (previous time as a federal banding sub-permittee strongly preferred), as well as experience in genetics, genomics, and/or bioinformatics. Consistent with our program scope and to advance an integrated understanding of adaptation in nature, we are especially interested in candidates who show promise to work in an inclusive and diverse collaborative environment and to engage intellectually across the diverse scales of genomes, phenomes, and environmental feedbacks. Individuals who are intellectually curious, responsible, willing to learn, team-oriented, and have attention to detail are encouraged to apply. An M.S. in a related field is preferred, but qualified candidates with extensive experience will be considered.
How to apply: Please send a cover letter describing your qualifications, including your commitment to diversity and inclusion in collaborative science, a curriculum vitae, unofficial transcripts, and the contact information for at least three references to Adrienne Kovach at email@example.com and indicate the position for which you would like to be considered. Please use “Ecological Genomics PhD Student Search” as the subject line of your email. All applications received before November 14, 2018 will receive full consideration, and applications will be accepted on a rolling basis thereafter until the positions are filled. A start date of January 2019 is strongly preferred, but is negotiable depending on the position in question.
The University of New Hampshire and the Department of Natural Resources and the Environment are home to a vibrant community of productive researchers with a strong commitment to student success (see http://www.unh.edu/unhtoday/2017/03/excellence-ecologyfor recent accolades). Located in the town of Durham, UNH is a beautiful campus surrounded by forest and natural landscapes. Only 30 minutes from the sea and less than 2 hours from the White Mountains, outdoor and other recreational activities abound, including right on campus.
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