The Marine Science Institute at the University of California Santa Barbara seeks a highly-motivated postdoctoral research associate. The position will focus on quantitative modeling of kelp forest community survey and environmental data to inform Marine Protected Area (MPA) performance in the state of California.
The postdoctoral research associate will be based in Santa Barbara, California but remote work is possible while COVID-related shutdowns remain in place. The postdoc will join a dynamic research group led by Dr. Jenn Caselle (https://labs.eemb.ucsb.edu/caselle/jennifer/) and will also collaborate extensively with researchers at UCSB and UC Santa Cruz (https://rclab.ucsc.edu/).
This position will work on analyses of kelp forest monitoring data to inform the evaluation and adaptive management of California's network of MPAs, with a focus on a statewide MPA network review in 2022. The specific goals and objectives of the evaluations are well defined in the CA MPA monitoring Action Plan (https://wildlife.ca.gov/Conservation/Marine/MPAs/Management/monitoring/action-plan#:~:text=The%20Action%20Plan%20prioritizes%20key,the%20goals%20of%20the%20MLPA.) but the Postdoc is encouraged to bring new and innovative perspectives to the research especially in the areas of MPAs and climate resilience. The Postdoc will lead development of the statistical analyses and participate in generating graphic and textual summaries, including but not limited to technical reports and peer reviewed publications. The postdoc will also support MPA evaluation analyses for several other projects in the lab including camera surveys of deepwater habitats and a catch and release fishing program. The successful candidate will be provided the opportunity to mentor junior scientists in our labgroup.
Basic Qualifications: A PhD (or equivalent) in ecology, marine biology, applied statistics, ecological modeling, or closely related discipline at time of application.
Preferred Qualifications: Ideal candidates will have advanced skills in data analysis, statistics, scientific coding/programming, and other quantitative methods. Experience with statistical analysis of spatial and temporal data (e.g. generalized linear models, generalized additive models, Bayesian approaches, machine learning), and species distribution modeling strongly preferred. Coding experience in R or equivalent programming language. Familiarity with spatial or geographic analyses. Demonstrated strong writing skills are required. Excellent verbal and written communication skills. A track record of talks and peer-reviewed publications appropriate to career stage. Familiarity with metadata documentation in eml and data and code repositories such as Github or DATAOne. Strong interpersonal skills to build and maintain strong relationships with academic, NGO and government partners, and to work effectively as part of a highly collaborative research team.
The position start date is as soon as possible, but ideally no later than October 31, 2020. The duration of the initial appointment is 12 months; reappointment for an additional year is possible. Salary is competitive, commensurate with the applicant's qualifications. Postdoctoral benefits are included (https://clients.garnett-powers.com/pd/uc/plans_benefits).
Applicants should submit a CV, a short (1-2 page) statement of research experience and interests related to the project, PDF copies of up to two relevant publications, preferably those that demonstrate statistical modelling examples, and the names, affiliations, and email addresses of three references to: https://recruit.ap.ucsb.edu/JPF01826
For primary consideration apply by September 4, 2020
Position will remain open until filled.
The University is especially interested in candidates who can contribute to the diversity and excellence of the academic community through research, teaching and service .
The University of California is an Equal Opportunity/Affirmative Action Employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.
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