Postdoc Fellow I - Social or Interdisciplinary Scientist
What You Will Do:
The newly formed NSF Artificial Intelligence (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 two (2) postdoctoral fellows, each for a two-year position, to conduct risk communication, risk perception, and decision-making research with a focus on AI/machine learning for weather, sub-seasonal to seasonal (S2S) climate, and coastal hazards. The postdoctoral fellows will conduct mixed-method, interdisciplinary research to identify and evaluate what trustworthiness means for expert users of AI in their risk management and decision-making contexts, and to iteratively guide and evaluate AI-information development and use.
The postdoctoral fellows will be affiliated with UCAR/NCAR and will work with Dr. Julie Demuth, co-lead of the risk communication research for the Institute. The fellows will also work closely with other institute personnel at NCAR and across AI2ES.
- Collaborate on developing interview data collection instruments to elicit attitudes, interpretations, and potential uses of AI for different expert groups (weather forecasters, emergency managers, transportation officials, other government users, private industry partners, etc.) across different weather, S2S, and coastal hazards. Collaborate on virtually conducting interviews, analyzing data, and synthesizing findings to guide development of trustworthy AI information.
- Collaborate on developing a Web-based randomized experimental design to systematically manipulate prototyped AI information and assess effects on experts’ interpretations, perceptions, and decision-making. Collaborate on virtually conducting experiment, analyzing data, and synthesizing findings to guide development of trustworthy AI information.
- Collaborate on developing research approach for summative, real-time evaluation of trustworthy AI information by expert groups as part of their real-world decision-making.
- Curate relevant literature with a focus on trust in risk, decision-making, and AI and other related literature, and ensure continued integration of the literature into the empirical research conducted.
- Interact with other postdocs, students, and researchers throughout the Institute in order to integrate research perspectives, co-analyze data, and co-develop meaning about implications of results.
- Prepare results for publication in peer-reviewed journals and for presentation at meetings and conferences. Help prepare and deliver summary reports or project progress reports as needed.
What You Need:
Education and Years of Experience
- Ph.D. degree within the last 5 years or expected within the next 6 months in a relevant social science or interdisciplinary field.
- Experience working in an academic or professional setting with interdisciplinary teams.
- General understanding of hazardous weather phenomena.
Knowledge, Skills, and Abilities
- Well-developed knowledge of qualitative and/or quantitative social science research methods, including design, implementation, and analysis of data.
- Experience conducting qualitative and/or quantitative data collection and analysis in a relevant social science or interdisciplinary field.
- Demonstrated ability to work independently and as part of an interdisciplinary research team.
- Skill in developing and implementing project plans to meet goals, objectives, and deadlines for deliverables.
- Excellent time management and organization skills.
- Effective written and oral communication skills, both within field of expertise and across disciplinary boundaries.
- Interest in the interactions between weather/S2S/coastal information and society and/or the interactions between AI/machine learning and society.
- Advanced ability in qualitative data analysis and/or statistical analysis.
- Familiarity with machine learning topics, as demonstrated by classes taken and/or research performed.