Jobs:Job-01750
Apply before: 6 January 2025
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Posting:
Position: Assistant/Associate/Full Professor
Apply before: 6 January 2025
Apply online:
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We are interpreting 'Environmental Systems' quite broadly, so please reach out with any questions you might have.
The search is open rank and the deadline is January 6th. https://apply.interfolio.com/158862
Best,
Daniella
Daniella Rempe Associate Professor; The University of Texas at Austin
Jackson School of Geosciences; Department of Earth and Planetary Sciences
EPS 2.202; 2275 Speedway, C9000; Austin, TX 78712512-471-5290 rempe@jsg.utexas.edu
http://www.daniellarempe.com
Assistant/Associate/Full Professor in Advances in Artificial Intelligence and Data Science for Environmental Systems:
The College of Natural Sciences (CNS) and the Jackson School of Geosciences (JSG) at the University of Texas at Austin invite applications for three tenure-track faculty positions as part of a cluster hire initiative, which aims to leverage advancements in artificial intelligence (AI) and data science (DS) to promote UT Austin’s leadership in addressing the world’s most pressing environmental challenges. It is broadly expected that the new faculty hires will likely be placed in the home departments of Integrative Biology (CNS) or Earth and Planetary Sciences (JSG), with the possibility of a joint appointment for Associate or Full Professor hires.
We encourage applications at all levels - Assistant, Associate, and Full Professor. We seek dynamic and collaborative scholars whose research advances or applies cutting-edge AI models and DS methods to analyze large observational data sets collected at various scales, develop predictive models of environmental systems, or develop technologies or approaches for environmental monitoring. Applications can include the pace and impacts of global change, conservation of biodiversity, management of water resources, and human, animal, and ecosystem health. Research areas of interest include, but are not limited to:
- Advancing AI-driven methods and big-data analytics to study and model complex environmental systems, from genes to populations to landscapes, particularly in the context of ecological and evolutionary processes, climate change, and other anthropogenic pressures.
- Applying AI and DS to improve mechanistic understanding of ecosystem dynamics, nutrient cycling, the climate system, and water and carbon cycling, from local to global scales.
- Using AI to drive innovation in remote-sensing technologies and data assimilation techniques to track environmental changes, monitor organisms, and integrate multi-source data (e.g., satellite, sensor networks, field observations) for enhanced environmental or ecological modeling and forecasting.
