Full-Time

Water Systems Fellow

University of Texas at Austin

University of Texas at Austin

Public research university in Austin, TX

Compensation Overview

$65k/yr

Company Does Not Provide H1B Sponsorship

Austin, TX, USA

In Person

PhD

Category
Academic & Institutional Research (1)
Required Skills
LLM
Graphics Processing Unit (GPU)
Python
High Performance Computing (HPC)
Neural Networks
Machine Learning
Data Analysis

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Requirements
  • A Ph.D. acquired within the last three years in Earth Science, such as Hydrology, Hydrogeology, Geology, or Geography, Civil Engineering, or a closely related field is required.
  • Demonstrated experience and aptitude in hydrology, including data analytics, data wrangling, data management, data synthesis, numerical and analytical modeling environments such as Python, geoprocessing and geographic information systems analytics, and data-driven model development.
  • Demonstrated ability to meet deadlines and effectively disseminate research project results to professional peers.
  • Excellent written and oral communication skills are required.
  • Professional demeanor and strong interpersonal skills are required.
Responsibilities
  • Perform hydrology research, including analysis and interpretation of large datasets using 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.
  • Provide support and service as needed to the supervisor, the Bureau, and the Jackson School.
  • Work in all weather conditions and extreme temperatures when required, including fieldwork as necessary.
  • Perform occasional weekend, overtime, and evening work to meet deadlines.
Desired Qualifications
  • Demonstrated aptitude and experience with predictive machine learning and deep learning techniques.
  • Experience with statistical analysis.
  • Hands-on experience using convolutional neural networks, generative artificial intelligence methods such as diffusion models, and interpretability techniques such as SHAP or LIME for explaining outputs of forecasting models.
  • Experience using high-performance computing systems with multiple nodes and graphics processing units.
  • Experience with 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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University of Texas at Austin is a public research university in Austin, Texas. It offers undergraduate, graduate, and professional education across a broad range of fields.

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