Professional, postdoctoral, and student opportunities
Interested in becoming part of the AI2ES Center? Open positions funded by the Center are listed here.
Contact links with details are provided in each listing. Apply to each position directly, not through this site.
Colorado State University
Postdoctoral Fellow: Risk Communication-AI Integration Scientist
Location: NOAA’s Global System Laboratory, Boulder, CO, USA
Are you interested in working at the intersection of Artificial Intelligence, risk communication, and weather and climate? Are you eager to work with an interdisciplinary team covering all of these disciplines and spanning academia, NCAR and NOAA? If so, this postdoctoral position might be a unique opportunity for you.
Description: The Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University is looking for a postdoctoral fellow. The postdoctoral fellow will be working on conducting integrated research at the intersection of AI, atmospheric science, and risk communication, risk assessment, and decision-making, working with CIRA, NOAA’s Global System Laboratory (GSL), University of Oklahoma, NCAR, and University of Washington. The work will leverage related ongoing research from the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). The fellow will be based in Boulder, CO, at NOAA’s Global System Laboratory (GSL).
Find out more and apply at https://jobs.colostate.edu/
When applying for this position please make sure to address in your cover letter how you meet the required job qualifications.
Full consideration deadline is currently listed as Oct 11, 2021, but will be extended by at least 2 weeks (Oct 25).
Texas A&M University – Corpus Christi
Supervisors: Dr. Scott King and Dr. Philippe Tissot
Ph.D. Graduate Student
Location: Corpus Christi, TX, USA
Description: Texas A&M University-Corpus Christi (TAMU-CC) seeks one Ph.D. graduate researcher with a prior degree in computer science, geospatial science, with some background in the environmental sciences. Additional experience in machine learning, physical science, or environmental science preferred. A master’s degree is preferred, however strong candidates with a BS will be considered. The student will join the research team of the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES.org) at the TAMU-CC Conrad Blucher Institute. The team has developed a deep learning model to predict coastal fog, FogNet (https://doi.org/10.1016/j.mlwa.2021.100038). The present model and its architecture were developed for a single location. The new student will investigate implementation of the model to other locations and different environmental settings while developing a broader set of predictors to include numerical weather predictions, satellite imagery and coastal measurements while gaining physical insights into the related coastal process using explainable AI (XAI). The candidate will work in close collaboration with other TAMU-CC PhD students and AI2ES partners at other universities, the local national weather service and the private sector. The student will need to be accepted to the TAMU-CC Geospatial Computer Science program or alternatively the TAMU-CC Coastal and Marine System Science program. The position comes with a monthly stipend, tuition and fees. Preferred start date is spring or summer 2022. To express interest or apply send your CV with an email sharing why you are interested in the position to Drs Scott King (firstname.lastname@example.org) and Philippe Tissot (email@example.com).
The University at Albany (UA) Atmospheric Sciences Research Center (ASRC)
Mentors: Dr. Christopher Thorncroft and Dr. Kara Sulia
Ph.D. Graduate Student
Location: Albany, NY, USA
The University at Albany (UA) Atmospheric Sciences Research Center (ASRC) in Albany, NY seeks two Ph.D. graduate research associates with background in machine learning and/or the physical sciences (preferably atmospheric science).
TWO AREAS OF RESEARCH
1. Regional Sensitivity to Winter Weather. The student will perform NY state holistic winter weather analysis, with focus on variations and sensitivities among climate regions and their influence on predictability. The student will be responsible for developing machine-learned models and employing other statistical techniques to identify patterns and pattern variability in winter weather events and impacts across the state. Forecast, reanalysis, and ensemble products (e.g., GFS, GEFS, NAM, HRRR) along with data from the NYS Mesonet, will serve as inputs, with the goal of assessing regional winter-weather predictability hours to days.
2. The Impact of Winter Weather on Roadways. The student will investigate the predictability of winter-weather effects on NY state roadways. The student will be responsible for developing machine-learned models and employing statistical techniques to identify patterns in meteorological (e.g., NYS Mesonet) and non-meteorological (e.g., traffic flow) datasets. The student will also be responsible for the visualization of results actionable to the end-user. Collaboration with NY State transportation sectors are expected, as well as emergency managers and decision makers.