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 dynamics to inform kelp restoration 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 position will work on a recently funded project that seeks to inform kelp restoration across the state of California. Utilizing extensive data (kelp forest surveys, oceanographic and benthic habitat) available from diverse sources across the state, the Postdoc will build statistical models to identify key ecological, oceanographic and management drivers of kelp (Macrocystis pyrifera and Nereocystis leutkeana) persistence or recovery at the scales of the entire state. Modeling will include but not be limited to generalized linear models, generalized additive models, and may include Bayesian approaches or machine learning. The candidate will also have experience with species distribution modeling. The region-specific, species-specific results from the modeling will serve to highlight places, times and techniques that stand the best chance of success for kelp restoration in California. The Postdoc will lead the development of a written and graphical restoration guide for use by managers, restoration practitioners, and funders. A background in coastal marine ecology is beneficial but not necessary.
Basic Qualifications: Applicants must have completed all requirements for a PhD (or equivalent) except the dissertation in ecology, marine biology, applied statistics, ecological modeling, or closely related discipline at time of application.
Additional Qualifications: PhD (or equivalent) in ecology, marine biology, applied statistics, ecological modeling, or closely related discipline by the time of appointment.
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. 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 code repositories such as Github. 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. This is a two year position. Start date is flexible and ideally no later than August 30, 2021. 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 (optional) preferably those that demonstrate statistical modelling examples, and the names, affiliations, and email addresses of three references to:
For primary consideration apply by April 30, 2021
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 as appropriate to this position.
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.