Postdoctoral Researcher: Supervised machine learning for upscaling and emulation of watershed models.
The University of Kansas is excited to announce a postdoctoral research opportunity to advance large-scale watershed modeling using machine learning methods to upscale and emulate results from existing high-resolution watershed models. This project is part of a USDA-funded, multi-institutional project to improve predictions of aquatic biological community responses to conservation actions in intensively managed agricultural watersheds. The postdoctoral position is full time for two years under the supervision of Amy Hansen. The successful candidate must have appropriate work authorization and be physically present by the start date of employment. The University of Kansas is in Lawrence, Kansas, a mid-size college town located next to the beautiful Kansas River.
60% - Advance watershed modeling. Use output from multiple existing subwatershed models to train and validate machine learning model. Identify and classify land response predictors of field-level model outputs across full basin using combination of GIS and machine learning model. Build river network model and couple with landscape model. Perform model validation and uncertainty analysis. Execute coupled upscale model under scenarios of climate and land use change.
20% - Planning and collaboration. Participate in planning, designing and conducting watershed modeling research projects under the direction of a faculty supervisor. Analyze and provide interpretations of evaluate cross-model and individual model results from scenarios and provide interpretations. Collaborate with a diverse project team from multiple institutions across United States to define overall project goals, share data and communicate results.
20% - Communicate science. Complete writing tasks, including literature review, required to publish research in peer-reviewed journals and, contribute toward the preparation of technical reports, papers and/or records. Present results at scientific conferences and to stakeholders. Contribute to the development, preparation and submission of externally funded proposals related to the research program.
This position is required to work on-site.
Required QualificationsPh.D. in an engineering or science fieldA strong background and interest in ecohydrology, water resources, and/or water quality, as indicated in application materialsProficient coding skills in Python as indicated in application materialsPrior experience with machine learning methods such as decision trees, random forest, ANN, etc. NOTE: To be appointed at the Postdoctoral Researcher title, it is necessary to have the PhD conferred at the time of hire. Appointments made without a diploma or certified transcript indicating an earned doctorate are conditional hires and are appointed on an interim basis not to exceed 6 - months. Upon verification of degree the appointment will be extended to its full duration.
Preferred QualificationsPrior experience using SWAT or other watershed models with watershed hydrological and biogeochemical modeling.Knowledge of watershed hydrological and biogeochemical process characterization.Experience working on interdisciplinary research projects and in a team environment.Strong quantitative skillsAbility to build, manage, and analyze large datasets using programming and/or statistical software.Familiar with geospatial analysis and associated tools (e.g., ArcGIS or GDAL)
Contact Information to Applicants
Professor Amy Hansen
Additional Candidate Instruction
A complete application consists of the online application, cover letter, resume, & 3 professional references. Only complete applications will be considered.
Application review begins March 4, 2021 and will continue until a pool of qualified applicants is received.
Advertised Salary Range
Application Review Begins
Anticipated Start Date
University of Kansas Lawrence Campus
U-Unclassified Professional Staff
Monday - Friday 8a.m. - 5p.m., additional hours as needed
Conditions of Employment
Contingent on Funding, Limited Term
Work Location Assignment
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