Jul 19, 2022

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 hiring a postdoc with expertise in machine learning and atmospheric science.  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.

Position Summary:    

The postdoc will be a key part of a team that is developing trustworthy AI for atmospheric science applications.  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.   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.

This position is part of a large multi-institutional institute and there will be postdocs located throughout the partner institutions.  The University of Oklahoma is the lead and the partners include Colorado State University, the University at Albany, the University of Washington, North Carolina State University, Texas A&M University-Corpus Christi, Del Mar College (Corpus Christi), the National Center for Atmospheric Research, Google, IBM, NVIDIA, Disaster Tech, and the National Oceanic and Atmospheric Administration.

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 including novel explainable AI and physically-based AI methods for environmental sciences.  This can also include work on the development of ethical guidelines for AI/ML for environmental sciences.
• 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.

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 [MA1] 
• 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

Application Details:

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.

This position is for one year, with the option to renew up to three years based on performance.