Researcher/Scientist III, Hydrology and Atmospheric Sciences

The University of Arizona's Department of Hydrology and Atmospheric Sciences brings together a rich legacy of innovation in both atmospheric and water sciences. Rooted in the Institute of Atmospheric Physics, founded in the early 1950s to advance research on cloud seeding in arid environments and the pioneering Department of Hydrology and Water Resources, established in 1966 as the nation's first independent hydrology department, our program reflects decades of leadership in research, education, and applied science. Since their formal merger in 2016, the department has embraced an integrated approach that unites atmospheric and hydrologic disciplines, building on long-standing collaborations to address complex environmental challenges and to train the next generation of scientists and professionals.

The University of Arizona's Department of Hydrology and Atmospheric Sciences invites you to apply for the role of Researcher/Scientist III, where you will contribute to advancing next-generation climate prediction capabilities. In this position, you will work to improve subseasonal-to-seasonal (S2S) climate predictability within the NOAA Unified Forecasting System (UFS) by enhancing representations of land-atmosphere interactions. You will identify and quantify key terrestrial hydrological and ecological processes that govern land "memory," and apply your expertise to improve the modeling of snow, soil moisture, and vegetation dynamics within the coupled NOAA UFS/Noah-MP system. Through this work, you will play a critical role in strengthening predictive tools that address complex environmental and climate challenges.

Outstanding U of A benefits include health, dental, and vision insurance plans; life insurance and disability programs; paid vacation, sick leave, and holidays; U of A/ASU/NAU tuition reduction for the employee and qualified family members; retirement plans; access to U of A recreation and cultural activities; and more!

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• Design and conduct long-term modeling experiments using the off-line (uncoupled) Noah-MP land model and subseasonal-to-seasonal (S2S) model predictions using the coupled UFS/Noah-MP model over the entire globe.
• Evaluate the coupled UFS/Noah-MP model's land memory using various ground-based and satellite remote-sensing data.
• Identify and quantify key hydrological and ecological processes contributing to land memory.
• Improve the model's representation of key processes and its S2S predictability.
• Participate in the acquisition of research funding by assisting with and contributing to grant proposals, supporting documents, and literature reviews.
• Write research progress reports, manuscripts, and articles and produce other research products (e.g., data products and model code).
• Train and advise PhD students in conducting modeling experiments and data analysis.

Knowledge,
Skills, and Abilities (KSAs):

• Strong skills in the coupled land and atmosphere modeling, preferably with the coupled UFS/Noah-MP model.
• Proficient in computer programming (e.g., Python, Fortran, C, MATLAB, and/or R).
• Skill in scientific writing, including preparing manuscripts for peer-reviewed journals.
• Skill in analyzing and managing large datasets and applying high-performance computing (Unix/Linux environments).
• Knowledge of machine learning techniques and their application in climate or hydrologic modeling.

This job posting reflects the general nature and level of work expected of the selected candidate(s). It is not intended to be an exhaustive list of all duties and responsibilities. The institution reserves the right to amend or update this description as organizational priorities and institutional needs evolve.

• Bachelor's degree in atmospheric sciences, hydrometeorology, hydrology or equivalent advanced learning attained through professional level experience required.
• Minimum of 5 years of relevant work experience, or equivalent combination of education and work experience.

• PhD in atmospheric sciences, hydrometeorology, or hydrology.
  
• Demonstrated experience handling large datasets and utilizing high-performance computing systems (Unix and Linux).
  
• Strong record of publishing papers in peer-reviewed journals.
  
• Demonstrated experience applying machine learning techniques.