Responsibilities

Duties of the Hydrologic Modeler will include the following:

• Model training & execution: train, execute, and synthesize large-scale physics-based and machine-learning hydrologic models across regional to continental domains.
• Synthesis & interpretation: analyze model results to characterize performance, flow signatures, and hydrologic behavior, and communicate findings to the modeling team.
• Calibration & evaluation: calibrate, benchmark, and validate models, quantify uncertainty, and diagnose where and why predictions succeed or fail.
• Forcing & training data: generate and quality-check model forcings and manage the training processes and datasets that drive these models.
• ML/AI methods: apply deep-learning approaches to hydrologic prediction, including tools such as NeuralHydrology and dHBV.
• Routing tools: help develop, test, and improve hydrologic and hydraulic routing tools, primarily in Python.
• Collaboration: contribute to the Modeling, Science, and Platform teams by sharing well-documented, reproducible code, methods, and results.

The Hydrologic Modeler selected should have the following:

• Master's in hydrology, water resources, engineering, hydrologic sciences, applied mathematics, computer science, or a related field; or a Bachelor's plus 2 years training and running hydrologic, physics-based, and/or machine-learning models; or equivalent experience.
• Background in hydrology, ideally including ecological or environmental flows, with an understanding of how water moves through watersheds and river systems.
• Strong Python skills, and experience with a deep-learning framework such as PyTorch.
• Understanding of model forcing generation and training workflows for large-scale models.
• Comfort working with large gridded and time-series datasets, Git, and Linux.

The Ideal Hydrologic Modeler will have the following:

• Experience with machine-learning hydrologic modeling tools such as NeuralHydrology and dHBV, or comparable physics-informed and differentiable modeling approaches.
• Experience developing or applying hydrologic and hydraulic routing methods.
• Familiarity with the Next Generation Water Resource Modeling Framework.
• Experience with high-performance or parallel computing for large-scale model runs.
• Track record of scientific writing, publication, or open-source contribution.
• Colorado Front Range presence for periodic in-person collaboration.