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Position Details

Academic Personnel

Postdoctoral Scholar- Teleconnected: Identifying tele-connected distributions using machine learning

Position Overview

School / Campus / College: College of the Environment

Organization: Aquatic and Fishery Sciences

Title: Postdoctoral Scholar- Teleconnected: Identifying tele-connected distributions using machine learning

Position Details

Position Description

The College of the Environment fosters existing and new collaborations between outstanding faculty, staff and students who are engaged in the study of: the solar system and Earth’s dynamic land, water and atmosphere; the development and application of environmental engineering and technological advances; and the impact of policy and human actions on the environment, and the management of natural resources.


The School of Aquatic and Fishery Sciences (SAFS) is dedicated to sustaining healthy marine and freshwater environments. Our school comprises one of the largest and most diverse academic aquatic and fisheries sciences programs in the United States. Our faculty conduct innovative research from the organism to the ecosystem scale, and are recognized leaders in aquatic biology, sustainable fisheries management, and aquatic resource conservation.


The School of Aquatic and Fishery Sciences values the strengths and professional experience that students, faculty, and staff bring to our community. We are committed to providing excellent education to all of our students, regardless of their race, gender, class, nationality, physical ability, religion, age, or sexual orientation. We are proud of the different roles that our students, staff, and faculty play in the community of the School and in the College of the Environment. We recognize that science is richer and the SAFS community is more vibrant when a diverse group of people participate in the SAFS community.


Tele-connected distributions are ubiquitous in ecology; events in one location impact events in other locations. This project will develop methods to identify tele-connected distributions with machine learning.  These methods could be used in many circumstances, including migration outcomes, connections in weather patterns, pollution impacts, and fishery dynamics. The case study for this project will be modeling larval dynamics in the Bering Sea by predicting distributions of young fish based on distributions of spawning fish that have a pelagic larval phase. Tele-connected distributions will be identified as clusters of spawning biomass and recruitment in space based on biomass, proximity, and other environmental covariates using clustering algorithms. Machine learning algorithms will be used to model each recruitment cluster based on the variability of the clusters of spawning biomass. The method will be applied to data for at least five Alaskan stocks, including snow crab (a stock recently declared overfished) and yellowfin sole.


The anticipated start date is December 1, 2022, but this is negotiable. The appointment is for 1 year. Applications should be submitted by October 15, 2022, but the position will remain open until filled. This is a full-time position located at the University of Washington in Seattle. The post-doc will be supervised by Dr. André Punt (University of Washington) and Dr. Cody Szuwalski at the Alaska Fishery Science Center.

Responsibilities include:

  1. Developing methods using established machine learning algorithms to identify tele-connected distributions
  2. Simulation testing the developed methods
  3. Applying the methods to five commercially fished species in the eastern Bering Sea
  4. Publishing the results in a peer-reviewed journal
  5. Presenting the results at a scientific meeting

The base salary range for this position will between $5,459 and $7500 per month, commensurate with experience and qualifications, or as mandated by a U.S. Department of Labor prevailing wage determination. 

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.



  • earned Ph.D. in Quantitative Ecology, Applied Statistics or a related field; and
  • proficiency in the R programming language;
  • experience applying machine learning techniques to ecological questions;



  • experience with spatial or spatiotemporal models;
  • experience with clustering algorithms;
  • experience developing simulation models to test statistical methods;
  • experience analyzing data from fisheries surveys; and
  • knowledge of the ecosystem and fisheries of Bering Sea.



To apply please submit your application through Interfolio with the following: (1) A letter of interest detailing your skills and experience. (2) A curriculum-vitae including publications. (3) Three letters of recommendation.

For questions about this position, including potential disability accommodations, please contact Kenyon Foxworthy, at


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.

Benefits Information

A summary of benefits associated with this title/rank can be found at Appointees solely employed and paid directly by a non-UW entity are not UW employees and are not eligible for UW or Washington State employee benefits.

Commitment to Diversity

The University of Washington is committed to building diversity among its faculty, librarian, staff, and student communities, and articulates that commitment in the UW Diversity Blueprint ( Additionally, the University’s Faculty Code recognizes faculty efforts in research, teaching and/or service that address diversity and equal opportunity as important contributions to a faculty member’s academic profile and responsibilities (

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Disability Services

To request disability accommodation in the application process, contact the Disability Services Office at 206-543-6450 or

Apply now

The University of Washington is using Interfolio's Faculty Search to conduct this search. Applicants to this position receive a free Dossier account and can send all application materials, including confidential letters of recommendation, free of charge.

For help signing up, accessing your account, or submitting your application, please check out Interfolio's help and support section or get in touch via email at or phone at (877)997-8807.

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