Postdoctoral Scholar in Quantitative Marine Ecology
Position overviewPosition title: Postdoctoral Scholar in Quantitative Marine Ecology
Open date: April 26, 2023
Most recent review date: Friday, May 26, 2023 at 11:59pm (Pacific Time)
Applications received after this date will be reviewed by the search committee if the position has not yet been filled.
Final date: Thursday, Apr 25, 2024 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.
Dr. Alexa Fredston in the Department of Ocean Sciences at the University of California, Santa Cruz (UCSC) invites applications for the position of Postdoctoral Scholar in Quantitative Marine Ecology.
The Fredston Lab uses large datasets and theoretical models to predict marine ecological responses to global change. The lab currently has two main themes: ecological forecasting (which focuses on modeling processes) and empirical biogeography (which focuses on explaining patterns).
The ecological forecasting theme includes the development and application of process-based “dynamic range models” for predicting near-term range shifts in marine species and testing fundamental drivers of range dynamics (see: https://youtu.be/B4aVoyzUWCI, https://doi.org/10.1111/gcb.16371).
The empirical biogeography theme uses large-scale historical datasets and a variety of tools (e.g., machine learning, eDNA) to understand why species’ ranges and range edges are where they are (see: https://doi.org/10.1111/gcb.15614, https://doi.org/10.1111/gcb.15035), and how global change is reshaping populations, communities, and ecosystems (see: https://doi.org/10.1073/pnas.160755111, https://doi.org/10.1111/nyas.13597).
While Dr. Fredston’s work has primarily used data on temperate fishes to date, she has broad interests in all marine taxa and systems.
This two-year postdoc position is funded by Dr. Fredston’s startup funds at UCSC, and thus represents a uniquely open-ended opportunity for the successful candidate to co-develop novel and independent research projects. The postdoc will also have opportunities to write grants, participate in working groups, join collaborations, engage with natural resource managers and conservation practitioners, and work with graduate and undergraduate students. The postdoc will have a primary research project such as:
- Field-testing dynamic range models in new geographies and settings—for example, adapting dynamic range models for the California Current, incorporating multiple data streams to improve forecast fits and accuracy, or using projected oceanographic conditions to develop future forecasting systems.
- Advancing the theory of dynamic range models—adding new processes (e.g., species interactions, oxygen limitation, local adaptation, stage-specific habitat use), exploring how they affect range shifts through simulation, and testing whether they help explain real data.
- Simulating whether biogeographic boundaries will act as barriers to range shifts—building spatial population models for a range of life histories that explore whether biogeographic boundaries that impede larval dispersal would prevent climate tracking.
- Exploring range edge dynamics in the Anthropocene—developing statistical methods to detect range edge positions in survey data and applying them to large-scale ocean biodiversity datasets to characterize recent range edge shifts.
The ideal candidate for this position will leverage their quantitative skill set and ecological expertise to lead exciting and novel research in marine biogeography, population ecology, or community ecology. The ideal candidate will also demonstrate the following qualities:
- Have expertise in theoretical and/or quantitative biology or ecology, or a closely related field.
- Have experience with one or more modeling approaches commonly used in ecology, such as: population modeling, spatio-temporal modeling, individual-based modeling, integral projection modeling, species distribution modeling, Bayesian hierarchical modeling, dynamic occupancy modeling, structural equation modeling, empirical dynamic modeling, and/or network modeling.
- Have familiarity with some form(s) of data and observations available for monitoring marine biodiversity, e.g., trawl surveys, acoustic surveys, visual surveys, tagging data, mark-recapture data, and eDNA.
- Be proficient in at least one open-source programming language, such as R or python.
- Have experience using version control software and platforms such as Git and GitHub.
- Be committed to open science principles, such as code review and data sharing.
- Have taken advanced courses in modeling, statistics, and data analysis.
- Have excellent communication skills.
- Have experience leading independent research projects, and a record of first-author publications (commensurate with experience) in related topics.
- Be excited about doing highly quantitative work that is relevant to natural resource management and biodiversity conservation.
- Contribute to a supportive and inclusive professional culture in the Fredston Lab.
Candidates who possess some but not all of the above areas of expertise are still encouraged to apply.
The Fredston Lab: https://www.alexafredston.com/
Ocean Sciences Department: https://science.ucsc.edu/department/ocea/
- Ph.D. (or equivalent foreign degree) in ecology, natural resources, environmental sciences, oceanography, applied mathematics, statistics, or a related field. Degree must be in hand at the time of initial appointment.
Curriculum Vitae - Your most recently updated C.V., which must include three professional references along with their contact information. The search committee will contact the references of those applicants who are under serious consideration.
Cover Letter - Letter of application (two page maximum) that describes your research experience, qualifications, and interest as it relates to the position (specifically in reference to the ideal candidate qualifications) and a proposed research project (applicants can either propose an independent research project or expand on one of the suggested research projects).
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