Postdoctoral Scholar in Machine Learning in Fisheries/Ocean Acoustics

University of Washington

United States

Postdoctoral Scholar in Machine Learning in Fisheries/Ocean Acoustics

Position Overview

Organization: Applied Physics Lab

Title: Postdoctoral Scholar in Machine Learning in Fisheries/Ocean Acoustics

Position Details

Position Description

See yourself at APL!

The Applied Physics Laboratory is a research unit at the University of Washington. Our research expertise is in ocean physics and engineering, ocean and medical acoustics, polar science, environmental remote sensing, and signal processing. We conduct research and development that is sponsored by a variety of federal and state agencies, and take great pride in our long-standing status as a US Navy-designated University Affiliated Research Center (UARC). Our work takes place not only on the University of Washington campus and medical centers, but in field locations around the world – at sea, in the air, and on polar ice caps. We apply rigorous scientific inquiry and engineering excellence in pursuit of solutions to important problems for the good of our region, nation, and world.

Diversity is a core value at University of Washington and the Applied Physics Laboratory shares this commitment. We are focused on building and sustaining an inclusive and equitable research environment for all students, staff, and collaborators. We believe every member on our team enriches our community by exposing us to a broad range of ways to understand and engage with the world, identify challenges, and to discover, design and deliver solutions.

We are looking for a highly motivated and independent individual to join the Echospace to conduct research at the intersection of fisheries/ocean acoustics and machine learning.

The postdoc will develop and apply machine learning methodologies to extract identification and distribution information of fish and zooplankton from large volumes of water column sonar data collected over the past 20 years off the west coast of the U.S. The project is in close collaboration with the NOAA Fisheries Northwest Fisheries Science Center (NWFSC) with a primary goal of developing a high-throughput echosounder data analysis pipeline to contribute to fishery stock assessment and ecosystem-based management. In addition, the postdoc will have opportunities to develop skills including cloud computing and software engineering as integrated components of their work within Echospace and interact with the vibrant ocean sciences and data science communities at UW and in Seattle.

The UW Echospace group, based jointly at the Applied Physics Laboratory (APL) and eScience Institute, is a highly collaborative research group with members with diverse backgrounds and experiences. Our research centers around acoustic ocean sensing and data science, and spans a broad spectrum from development of computational methods, open source software, cloud applications, to joint analysis of acoustic observations and ocean environmental variables. We are committed to provide a supportive environment for group members to grow and contribute to the acoustics, oceanography, and data science communities.

The anticipated start date of this position is in the Fall of 2021. The initial appointment period is 18 months, with renewal contingent on satisfactory performance and funding availability (also see Limitations on Appointment below). Promotional opportunities following the completion of the postdoc appointment are possible subject to availability of funds and demonstration of an interest in developing independent research initiatives during the appointment. The candidate is encouraged to apply for the UW Data Science Postdoctoral Fellowship and the APL Science & Engineering Enrichment and Development (SEED) Postdoctoral Scholar Program in parallel with this position.

Qualifications

The candidate will hold a PhD degree (earned or nearing completion of) in acoustics, oceanography, computational sciences, or a related field, and have a track record of first-author, peer-reviewed publications, programming experience (Python, R, Matlab, or similar), and prior research experience in relevant research domains, such as ocean acoustics, fisheries acoustics, machine learning, statistics, data visualization, and physical or biological oceanography. Familiarity with echosounder data and analysis techniques, and experience applying machine learning methods to scientific problems are preferred. They are expected to have excellent communication skills, the experience and/or ability to work in a team environment, and a strong interest in interdisciplinary research approaches.

Instructions

Each applicant will be expected to submit:

1) CV including a list of publications (include GitHub handle if available)

2) Cover letter with brief description of present and future research interests

3) Three references and their contact information

To request disability accommodation in the application process, please contact the UW's Disability Services Office at 206-543-6450, or 206-543-6452 (TTY), or dso@uw.edu.

For questions about the position duties, please reach out to Drs. Wu-Jung Lee.

Limitations on Appointment

In compliance with regulations at University of Washington, cumulative length of postdoctoral appointment may not exceed 5 years, including postdoctoral experience(s) at other institutions. Postdoctoral Scholars are engaged in full-time mentored advanced training to enhance professional skills and research independence, and perform primarily research and scholarship under the direction and supervision of University faculty mentors.

Postdoctoral scholars are represented by UAW 4121 and are subject to the collective bargaining agreement, unless agreed exclusion criteria apply. For more information, please visit the University of Washington Labor Relations website (UAW Contract).

Equal Employment Opportunity Statement

University of Washington is an affirmative action and equal opportunity employer. All
qualified applicants will receive consideration for employment without regard to race, color, creed,
religion, national origin, sex, sexual orientation, marital status, pregnancy, genetic information,
gender identity or expression, age, disability, or protected veteran status.

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