CIRES / NOAA Physical Sciences Laboratory: Postdoctoral Associate Developing Statistical Methods to Improve Weather and Hydrologic Predictions

The Cooperative Institute for Research in Environmental Sciences (CIRES) is seeking a Postdoctoral Associate to conduct research with a collaborative team toward enhancing ensemble hydrologic predictions at weather to seasonal timescales. The candidate will develop, test, and apply advanced statistical and/or machine learning methods to prepare meteorological output from global ensemble prediction systems for water forecasting applications. The project’s outputs will serve a diverse community of users including fellow researchers in hydrometeorological forecasting communities, operational streamflow forecasting entities, and water resource decision-makers.

What Your Key Responsibilities Will Be

• Develop and/or apply statistical/machine learning post-processing or downscaling techniques to prepare ensemble numerical weather predictions for use in a hydrologic prediction system.
• Establish data-processing workflows to obtain, manipulate, and visualize ensemble model output.
• Work with a team of scientists to improve skill, reliability, and interpretability of hydrometeorological ensemble forecasts for targeted applications (e.g., water availability, floods, droughts, etc.).Provide scientific and technical direction for experimental designs to understand and characterize hydrologic forecast performance through improvements in meteorological forcings.
• Prepare and present results to scientific audiences and to stakeholders via journal publications, conference presentations, and/or user-group meetings.

What We Require

• A PhD in atmospheric, hydrologic, statistics, or other Physical Sciences, Applied Mathematics, or a related discipline.
• Experience using Linux and developing structured code in Python, R, and/or shell scripting.

What You Will Need

• Expertise in hydrometeorology, hydroclimatology, statistics, or data science.
• Hands-on knowledge or familiarity with ensemble meteorological datasets.
• Interest in ensemble hydrometeorological forecasting.
• Ability to work and communicate effectively within a team environment and to facilitate communications across multiple teams and multiple organizational units.

What We Would Like You to Have

• Proficiency with statistical and/or machine learning methods.
• Experience with forecast calibration and verification.
• Experience and/or interest in GPU computing for machine learning applications.
• Experience with scientific analysis and visualization tools.

To apply, please submit the following materials:

• Resume or CV;
• Cover letter addressed to the Search Committee briefly describing your qualifications, professional goals, and specific interest in this position;
• Although not required at the time of application, please be ready to submit contact information for five professional references (must include at least two supervisors). If you are identified as a finalist for this role, you will be asked to invite your references to complete a SkillSurvey questionnaire on your behalf. SkillSurvey is an online reference check solution. All information is kept confidential and shared only with the Search Committee members.
• Optional: Transcripts/Proof of Degree.