Post-Doctoral Research Fellowship in Quantitative Conservation Ecology and Machine Learning
FELLOWSHIP DESCRIPTION AND DUTIES:
The Elizabeth Madin Laboratory at the University of Hawai‘i at Mānoa’s Hawai‘i Institute of Marine Biology is seeking to recruit a postdoctoral fellow in the area of quantitative conservation ecology to join our lab. The position will be funded by a recent NSF CAREER award focused on decoding seascape-scale vegetation patterns on coral reefs to understand ecosystem health.
RESEARCH CONTEXT AND SPECIFIC FOCUS:
Human impacts on coral reefs continue to accumulate from a variety of sources, including climate change, fishing and other types of resource extraction, pollution, habitat alteration, and others. Meanwhile, recent advances in remote imaging (e.g., high-resolution satellite and drone imagery), tracking (e.g., automated vessel tracking), and other technologies allow quantification of changes to human activities and coral reef ecosystems in near-real time over local to global scales.
The successful candidate will design and lead research investigating human impacts on coral reefs through the use of new and emerging tools capable of rapidly advancing coral reef science and conservation. Specifically, the postdoc will take a leadership role in 1) the creation of the machine learning algorithms for automated detection and measurement of reef halos from high-resolution satellite imagery, and 2) the integration of results arising from these methods with existing datasets. The postdoc will also be encouraged and supported to develop/execute their own broadly-related research questions (see “Appointment and application” below). Additionally, the postdoc will be actively engaged in mentoring of graduate students/interns and possibly teaching of graduate students.
A Ph.D. in computer science, quantitative geography/spatial data analysis, mathematical biology, statistics, quantitative ecology, or oceanography;
Publication record that illustrates an ability to conduct novel, independent research;
Considerable experience processing, manipulating, and analyzing large datasets;
Demonstrated proficiency with R (or related programming languages) and with software tools for analyzing geospatial data;
Demonstrated aptitude for applying advanced computational tools in a research setting;
Excellent problem-solving skills;
Excellent time management skills, including the ability to meet project goals in a timely manner and follow projects through to completion, and meticulous work style, as evidenced by previous research;
Demonstrated ability to mentor, or an interest in mentoring, junior laboratory members;
Strong interpersonal and communication skills, including the ability to work both independently and collaboratively, and to communicate research findings at professional meetings and in high-quality peer-reviewed journals.
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