Full-Time

Postdoctoral Fellow in Water Systems

Ml

University of Texas at Austin

University of Texas at Austin

Compensation Overview

$65k/yr

Company Does Not Provide H1B Sponsorship

Austin, TX, USA

In Person

Category
AI & Machine Learning (2)
,
Required Skills
Python
Data Analysis
Requirements
  • Ph.D. acquired within the last three years in Earth Science (e.g. Hydrology, Hydrogeology, Geology, Geography), Civil Engineering, or closely related field.
  • Demonstrated experience and aptitude in a hydrology context with: (a) data analytics, wrangling, management, and synthesis, (b) numerical and analytical modeling environments (e.g. Python), (c) geoprocessing and GIS analytics, and (d) data-driven model development.
  • Demonstrated ability to meet deadlines and effectively disseminate research project results to professional peers.
  • Excellent written and oral communication skills.
  • Professional demeanor and strong interpersonal skills.
  • Degree must have been obtained within 3 years from date of hire.
Responsibilities
  • Perform research in the hydrology field, including analysis and interpretation of large datasets using various analytical, statistical, and numerical techniques.
  • Contribute to the publication of scientific papers and presentation of findings to scholarly meetings and stakeholders.
  • Collaborate and coordinate with research program team members and participate in program development through proposals and other fundraising.
  • In addition to other duties as assigned, provide support and service as needed to supervisor, the Bureau, and the Jackson School.
Desired Qualifications
  • Aptitude and experience with: (a) predictive machine and deep learning techniques, (b) statistical analysis, (c) hands-on experience using models such as convolutional neural networks (CNNs), generative AI methods such as diffusion models, and interpretability techniques commonly applied in hydrology including SHAP or LIME for explaining outputs of forecasting models, (d) experience in using high performance computing systems with multiple nodes and GPUs and (e) drought metrics.
  • Familiarity with Texas water resources and management practices.
  • Experience working within an integrated team of scientists, engineers, and economists in a dynamic environment.
University of Texas at Austin

University of Texas at Austin

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