Cluster Hire - Advances in Artificial Intelligence and Data Science for Environmental Systems
- Employer
- UT Austin, Geology dept
- Location
- Austin, Texas (US)
- Closing date
- Jan 26, 2025
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- Discipline
- Global Environmental Change, Interdisciplinary/Other
- Career Level
- Any Experience Level Considered
- Education Level
- PhD
- Job Type
- Full-time
- Relocation Cost
- Negotiable
- Sector Type
- Academia
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.
UT Austin is home to state-of-the-art research facilities including a growing field station network, world-class collections, and unrivaled high-performance computing resources at the Texas Advanced Computing Center (TACC). Researchers benefit from collaborative opportunities in AI/DS through the UT Institute for Foundations of Machine Learning, the Machine Learning Laboratory, the new UT Center for Generative AI, and the Oden Institute for Computational Engineering and Sciences. UT’s location in Austin—a growing tech and innovation hub— creates opportunities for collaboration with industry leaders in technology and AI. The Environmental Science Institute facilitates interdisciplinary research, education, and community engagement across the geosciences and biosciences.
The Department of Integrative Biology is renowned for its interdisciplinary approach, combining expertise in ecology, evolution, and behavior to address fundamental questions about organisms in their natural environments. Faculty members are leaders in diverse research areas such as biodiversity, genetics, evolution, conservation biology, disease ecology, spatial ecology, and theoretical ecology, often integrating approaches and collaborating across disciplines. Faculty in Integrative Biology supervise students in Ecology, Evolution, and Behavior (EEB), Plant Biology, Cell and Molecular Biology, Statistics, and Computational Science, Engineering, and Mathematics (CSEM) Ph.D. programs.
The Jackson School of Geosciences hosts a vibrant and innovative research community of more than 125 faculty and offers access to world-class research facilities and support. More than 50 JSG faculty engage in cutting-edge environmental research, leveraging unique facilities. JSG is a leader in developing and using remote and in-situ sensing technologies for autonomous monitoring of environmental systems and calibration of satellites. In addition to their highly ranked undergraduate and graduate programs, the JSG offers a graduate certificate in AI and Machine Learning.
Qualifications:
- A Ph.D. in Earth Sciences, Ecology, Evolutionary Biology, Climate Sciences, Computer Science, Data Science, or a related field is required at the time of appointment.
- A demonstrated track record of developing or advancing the use of novel AI methodologies with applications in environmental science.
- Proven ability to engage in interdisciplinary research and collaborate with experts across computational and environmental disciplines.
- Strong potential or evidence of securing external funding to support an internationally recognized research program.
- Commitment to teaching and mentoring students at the undergraduate and graduate levels, with an emphasis on integrating research and teaching.
Application Instructions: Applicants should submit a cover letter (1 page); curriculum vitae; a research statement detailing past accomplishments and a vision of future work (up to 3 pages); a teaching statement (up to 2 pages); a mentoring statement (up to 2 pages); and contact information for three references. Applicants are encouraged to specify a preferred home department in the cover letter.
Applications received by January 6, 2025 will be assured of full consideration.
Equal Employment Opportunity Statement
The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.
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