Postdoctoral Fellow in Remote Sensing Environmental-Social Change

The University of Texas at Austin is seeking a Postdoctoral Fellow in Remote Sensing Environmental-Social Change. This remote position requires a PhD in Earth Science or a related field, with expertise in remote sensing analysis, data analytics, and machine learning. The role involves mapping environmental changes in disadvantaged Texas communities and contributing to research projects. Responsibilities include data analysis, collaboration, publication, and stakeholder engagement.
About the position

We are seeking a PhD researcher with a solid background in remote sensing imagery analysis, scripting, programming, statistics, data analytics, and numerical modeling in the context of hydrology and environmental science. This position will support existing and forthcoming projects in the BEG water resources group. This position is at the J.J. Pickle Research Campus in North Austin. We are looking for a highly motivated researcher to fill a Postdoctoral Fellow position (minimum one-year term with extensions subject to performance and research program needs/funding). The successful candidate will assist with a new research program focused on applying remote sensing imagery to map environmental and social changes in disadvantaged communities (colonias) in Texas. A key focus of the research program is to utilize remote sensing data (e.g., Landsat, Sentinel-2, Planet), and machine learning techniques to map changes in these communities through time. The successful candidate will become an integral part of the BEG Water Resources Group and contribute to impactful research addressing critical environmental and social issues in Texas colonias.

Responsibilities
• Work with remote sensing datasets, including Landsat and high-resolution products.
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• Apply data analytics and machine learning techniques to map and interpret data using geoprocessing tools and GIS platforms, including Google Earth Engine and ArcPy.
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• Map the evolution of colonias over time and analyze social and environmental stressors, including floods, on resident populations.
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• Contribute to publication of scientific papers, present findings at scholarly meetings, and coordinate with stakeholders.
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• Collaborate with research program team members and participate in program development through proposals and other fund raising.
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• Provide support and service, as needed, to the supervisor, BEG, and the Jackson School, when possible.

Requirements
• Ph.D. awarded May 2022 or later in Earth Science (e.g. Hydrology, Hydrogeology, Geology, Geography), Civil Engineering, or closely related field.
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• Expertise in remote sensing data products and their environmental applications.
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• Proficiency in data wrangling, analytics, synthesis, and management.
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• Knowledge of machine learning approaches, programming languages (e.g., Python, R, Matlab) and geoprocessing analytics (e.g., ArcGIS, QGIS, Google Earth Engine).
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• Adaptability to apply skills in environmental data analytics to other topics related to water resources and the environment.
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• Proven ability to effectively disseminate project results to peers and stakeholders.
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• Experience working within an integrated team of scientists, engineers, and economists in a dynamic environment.
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• Experience meeting deadlines.
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• Excellent written and oral communication skills.
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• Professional demeanor and excellent interpersonal skills.

Nice-to-haves
• Project relevant experience and demonstrated aptitude with: (a) assembling and synthesizing disparate remote sensing products for mapping building/infrastructure development, (b) assessing vulnerability to environmental hazards such as flooding, (c) artificial intelligence/machine learning techniques applied to mapping urban development, land cover change, building footprints, and flood exposure.
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• Familiarity with social vulnerability metrics.

Benefits
• Retirement plan eligibility through Teacher Retirement System of Texas (TRS) or Optional Retirement Program (ORP).
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• Potential for field work.
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• Opportunity to work in a dynamic research environment.