University of Washington

Postdoctoral Scholar - Machine Learning for Extreme Weather Events

University of Washington$80K — $81K *
Education, Government & Non-Profit
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • PhD or foreign equivalent in Atmospheric Science or related field.
  • Experience with weather and/or climate models.
  • Familiarity with machine learning methods for weather and climate emulation.
  • Proficient in Python, Linux, and agentic workflows.
  • Proven analytical and writing capabilities with peer-reviewed publications.
  • Ability to work independently and collaboratively in a team.

Responsibilities

  • Investigate extreme climate events using historical data.
  • Utilize machine-learning weather models to analyze climate variability.
  • Collaborate with a multi-university research team.
  • Contribute to the understanding of heatwaves, coldwaves, and tropical cyclones.
  • Communicate findings through reports and scientific publications.

Benefits

  • Full-time (100% FTE) position.
  • Initial 1-year appointment, with potential renewal for up to 2 years.
  • Collaboration with a team across three universities.
  • Opportunities for skill development in machine learning and climate science.
Full Job Description
Description

Professor Greg Hakim in the Department of Atmospheric and Climate Science at the University of Washington is seeking a full-time (100% FTE) postdoctoral scholar to investigate extreme events in historical climate. Building on reconstructions of the climate of the last millennium, we seek to understand the range of variability of extreme heatwaves, coldwaves, tropical cyclones and other weather events. This research involves the use of machine-learning weather models for sampling these extreme events conditioned on the climate state. This position is part of a larger team spread across three universities with the common goal of understanding weather events constrained by historical climate variability.

This position is full-time (100% FTE) with an initial term appointment of one year (12 months), renewable depending on funding and/or satisfactory performance for a total period of up to 2 years. The salary for this position will be $6,681-$6,761 per month, commensurate with experience and qualifications, or as mandated by a U.S. Department of Labor prevailing wage determination. The position is available starting March 6, 2026. The review process will commence immediately upon receipt of applications and will continue until filled. The anticipated start date is June 1, 2026. Priority consideration will be given to applications received by April 1, 2026.

Qualifications

Minumum Qualifications

  • Candidates must have completed a PhD or foreign equivalent in Atmospheric Science or a related discipline.
  • Experience running weather and/or climate models.


Desired Qualifications

  • Experience with machine learning methods, including emulators of weather and climate dynamics.
  • Experience with modern computing environments, including Python, Linux, and agentic workflows
  • Strong analytical and writing skills as demonstrated by a record of publication in peer-reviewed journals.
  • Experience working effectively independently and in a collaborative group.


Application Instructions

To apply, candidates will submit materials via Interfolio, comprising

  • A letter of interest describing their skills, experience, and research interests
  • A curriculum vitae, including a list of publications and links to any open code repositories
  • Two representative publications
  • The names and contact information for three references. PDF files are preferred.

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