Sign up for this fully-funded postdoctoral fellowship for up to three years in the area of quantitative conservation ecology and machine learning.
Set to take place at Hawaii, Mānoa, the selected postdoctoral fellow will join the Elizabeth Madin lab for a funded position while leading joint research on the selected areas.
Read on to know if you or someone you know fits the description for this position.
Application Deadline: November 15, 2020.
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The successful candidate/postdoctoral fellow 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.
- Take a leadership role in
- the creation of the machine learning algorithms for automated detection and measurement of reef halos from high-resolution satellite imagery
- the integration of results arising from these methods with existing datasets.
- Receive support to develop/execute their own broadly-related research questions.
- Be actively engaged in mentoring of graduate students/interns and possibly teaching of graduate students.
To be eligible for application, the following qualifications are required:
- A Ph.D. in computer science, quantitative geography/spatial data analysis, mathematical biology, statistics, quantitative ecology, oceanography, or related field
- 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.
The following qualifications are largely desired:
- Experience in the development of machine learning algorithms
- Experience participating in or leading marine field expeditions, including the use of snorkelling and SCUBA diving
- Experience as a small boat operator
- Ability to travel internationally for fieldwork and/or conferences.
- Applications from STEM-underrepresented minorities (particularly people of Hawaiian and other Pacific Islander backgrounds), women, and persons with disabilities are strongly encouraged.
- Applicants from overseas are also encouraged to apply.
- Quantitative skills are required; experience with AI/machine learning algorithms is not, but is a bonus.
- Fellowship stipend is commensurate with experience, within guidelines set by the university.
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To apply, forward the following details via email to Dr. Madin: email@example.com in a single PDF document, with the file name “MadinLabPostdocApplication_YourLastName” your last name in the file name and the email subject heading “Madin Lab postdoc application”:
- A cover letter briefly explaining your motivation for applying for this fellowship, how your prior research experience qualifies you for the fellowship, and how you satisfy the required and desirable qualifications (in dot-point format);
- A short (1-2 page) research proposal (including hypotheses, objectives and methods) describing the independent research project(s) you would like to undertake (proposals that are creative, scale-able, and relevant to coral reef conservation are encouraged);
- A CV, including publication list (which may include publications in advanced stages of preparation that will be likely in the review process by the above postdoctoral fellowship deadline date) and names and contact details for three references.
About the Elizabeth Madin Lab Post-Doctoral Research Fellowship in Quantitative Conservation Ecology and Machine Learning
The Elizabeth Madin Lab Post-Doctoral Research Fellowship in Quantitative Conservation Ecology and Machine Learning is a post-Doctoral Research fellowship administered by the Elizabeth Madin Laboratory at the University of Hawai‘i at Mānoa’s Hawaii, Institute of Marine Biology.
The specific focus of the Research is human impacts on coral reefs. 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.
In a family-friendly, flexible work environment where work-life balance is encouraged, the research led by the postdoctoral fellow and the Elizabeth Madin team will study the creation of the machine learning algorithms for automated detection and measurement of reef halos from high-resolution satellite imagery.
Also, the integration of results arising from these methods with existing datasets.
The Madin lab provides a supportive and stimulating research environment with a group of highly collegial scientists who are committed to scientific outreach and policy, to open science, and to enhancing diversity in STEM.
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.
Key Dates and Order of Program
The due date for applications is November 15, 2020.
The postdoctoral fellowship is for one year, but is renewable for up to three years based on performance and funding availability.
The start date is flexible, but would ideally be no later than July 2021.
The primary criteria for selection of candidates are:
- Quality and originality of the research proposal
- Individual qualifications and background
- Academic excellence