Computational and Hydro-ecological Modeling Postdoc

  • Full Time
  • Anywhere

Postdoctoral fellowship to model perturbations in hydro-ecological systems driven by shifts in snow, climate, and vegetation at the Los Alamos National Laboratory.

The Computational Earth Science Group in the Earth and Environmental Sciences (EES) Division at Los Alamos National Laboratory has an immediate opening for a creative and resourceful postdoc candidate with substantial knowledge of hydrology models, particularly in conjunction with understanding of changing snow and streamflow impacts, extreme events, and perturbations affecting land surfaces, such as vegetation disturbances, vegetation stress and forest mortality. The project involves a combination of computer modeling and simulation, integrating observations, including remotely sensed data, and high-performance computing.

The candidate will work as part of a research team conducting work in watersheds across the US, including Alaska. Successful applicants will have hydrologic modeling and analytical science expertise in one (or more) of three core areas of the project:

Watershed modeling, with the intent of understanding interactions and feedbacks between climate and key physical processes driving hydro-ecologic systems at the local to regional scale.

Impact of climate on land surface hydro-ecology, including snow, rain and temperature drought, vegetation disturbances, and other perturbations events that influence snow accumulation, snow melt, and streamflow responses.

Approaches to understanding and validating model processes and observations at various scales, including techniques to integrate ground-based and remotely sensed observations of snow and vegetation into models.

Minimum job requirements:

Strong computational and hydro-ecological modeling skills.

Existing knowledge of hydro-ecological process modeling at local-to-regional scales.

Existing knowledge of climate effects, including snow and rain processes.

Coding proficiency in languages such as Julia, Python, R, MATLAB, C/C++, Fortran, and Java.

Excellent communication, writing, and oral presentation skills.

Desired skills:

Knowledge of remote sensing of snow and vegetation.

Knowledge of statistical approaches to understanding changing hydro-ecology, including extreme events, uncertainty, and sensitivity analysis.

Understanding and ability to work in high-performance computing (HPC) environments.

Experience in big-data processing and analyses.

A publication and presentation record including first-authored and collaborative efforts.

Education and Background:

Candidates must have received a Ph.D. within the last five years (or pending) from a field related to the job requirements, including hydrology, snow science, or water resource engineering. Candidates must have demonstrated skills and background in topics detailed in minimum job requirements, and preferred candidates will possess one or more of the desired skills, along with the ability to work in a diverse and dynamic team.

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