Postdoctoral Research Associate
Job posting number: #7192868 (Ref:if128312)
Posted: November 10, 2023
Basic Purpose/Job Function:
The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) is an NSF funded AI Institute that brings together universities, government, and private industry to develop trustworthy AI for environmental science. AI2ES will uniquely benefit humanity by developing novel, physically based AI techniques that are demonstrated to be trustworthy, and will directly improve prediction, understanding, and communication of high-impact environmental hazards.
We are looking for a postdoc with expertise in machine learning and atmospheric science and interest or experience in working in interdisciplinary teams with social scientists. This postdoc will be located at the University of Oklahoma in Norman OK and will work with Dr. Amy McGovern, the PI and director of the AI2ES institute. The postdoc will also collaborate with other institute personnel at OU in the School of Computer Science and the School of Meteorology as well as with personnel across AI2ES, the National Center for Atmospheric Research, and Stanford University.
The postdoc will be a key part of a team that is focused on developing trustworthy AI for predicting and understanding flooding and people’s reactions to potential flood events. The postdoc will work with interdisciplinary collaborators across the institute. At OU, this will include working with the School of Computer Science and the School of Meteorology as well as colleagues at NOAA and the National Center for Atmospheric Research, and Stanford University. This is an exciting position that will allow the postdoc to work at the forefront of the development of trustworthy AI for a variety of environmental science applications.
AI2ES is strongly committed to advancing diversity, equity, and inclusion. Candidates are expected to have the ability to advance the DEI mission and should address this in their statement.
Expected job duties
- Research in trustworthy AI for environmental sciences (70% time)
- The postdoc’s main duties are to develop and test trustworthy AI techniques focused on understanding and predicting people’s behavior around major flooding events.
- Knowledge transfer and institute level integration (15% time)
- The postdoc is expected to integrate with the other postdocs, students, and researchers throughout the institute. If possible (post-pandemic), this may involve travel to other institute sites. It will also involve video meetings to other sites. Additionally, the postdoc may be involved in working with students.
- Publishing and Sharing results (15% time)
- The postdoc is expected to be actively publishing and presenting results at both AI conferences and journals and atmospheric sciences and journals. This may involve travel.
This position is for one year, with the option to renew up to two years based on performance.
Salary Range: 75,000-80,000
Required Job Qualifications
- Ph.D. in Machine Learning/Artificial Intelligence and experience with atmospheric sciences OR a PhD in Atmospheric Science or closely-related field and experience with Machine Learning/Artificial Intelligence. Note the PhD does not need to be completed to apply for the position but must be completed prior to beginning the postdoc.
- Experience with large spatiotemporal data sets
- Demonstrated experience of applying at least one machine learning or advanced statistical technique to an atmospheric-science-related problem.
- Demonstrated curiosity, creativity and enthusiasm to learn new skills.
- Experience pursuing research, both independently and as part of a research team.
- The successful candidate must be legally authorized to work in the United States by the desired start date. AI2ES cannot provide visa sponsorship for this position.
- Experience with neural networks/deep learning
Preferred Job Qualifications
- Experience with large atmospheric science data sets
- Prior research studying atmospheric variability, extreme weather, or subseasonal-to-seasonal prediction.
- Prior knowledge of a broad range of ML topics, as demonstrated by classes taken and/or research performed.
- Prior experience collaborating across disciplines.
- Experience with tensorflow (preferred) or pytorch
- Experience with HPC systems
- Experience or interest in working in interdisciplinary teams, including with social scientists
Special Instructions to Applicants
In your cover letter, please address all of the required and preferred qualifications. A complete application will include a cover letter, resume or CV, and three professional references. References of finalist candidates will be contacted for letters of recommendation; candidates will be notified prior to their references being contacted.
The successful candidate must be legally authorized to work in the United States by the desired start date. AI2ES will not provide visa sponsorship for this position.
If you have a need for hybrid or remote work, please mention that in your cover letter.