The ideal candidate will have strong experience in processing and analyzing eDNA samples, especially using metabarcoding, qPCR, and/or ddPCR. Experience in data analysis and modeling of eDNA data and knowledge of common molecular laboratory techniques is highly desirable.
This position will require strong leadership skills, excellent communication skills, and the ability to work with multi-disciplinary teams of collaborators from academic, federal, and state fisheries labs.
Responsibilities:
- Lead research projects related to improving quantitation of eDNA metabarcoding data
- Conduct laboratory work including eDNA metabarcoding and species-specific assays
- Bioinformatic analysis of sequence data to assign taxonomy to amplicon sequence variants
- Employ analytical and modeling approaches to correct sequence read counts to align with complementary fisheries collection data
- Organize and maintain a sample archive and database
- Clearly and efficiently communicate project results to a variety of audiences, including project collaborators and the general public
- Lead the publication of results in peer-reviewed scientific journals and present research findings at scientific conferences
- Contribute to grant proposals, institutional fund-raising activities and community outreach activities
- Mentor junior members of the team when appropriate
Qualifications:
- Ph.D. in molecular biology, genomics, biology, fisheries science, or a related field
- Familiarity with the complete eDNA workflow from sample collection to data analysis/modeling and publication
- Experience with common eDNA molecular processing techniques such as eDNA extractions, PCR/qPCR/ddPCR, library preparation, and Illumina sequencing
- Knowledge of single-cell and/or whole-genome sequencing library preparation, unique molecular identifiers, and/or flow cytometry is desirable
- Experience generating and analyzing eDNA data including metabarcoding and species-specific assays
- Understanding of fisheries biology, management, and the stock assessment process
- Strong computational skills and experience with applying occupancy, Bayesian, or other statistical models to genomic data
- Proficient in High Performance Computing clusters, Linux environments, shell scripting, and applying Perl, Python, and R for biological data analysis Creative and inquisitive person eager to think outside the box
- Strong publication record representing innovative, high-quality research
- Excellent communication and teamwork skills
- Demonstrated record of leadership, integrity, and adaptability
