Postdoctoral Fellowship with Professor Ram Rajagopal, Department of Civil and Environmental Engineering

The postdoctoral fellow will join the Stanford Sustainable Systems (S3L) Lab and work on a specific project to develop NLP models to track granular information on climate change impact on people/infrastructure as well as residential clean energy technology adoption (e.g., EV chargers, batteries, electric heating) from widely-available textual data (e.g., news articles, building permits, social media posts). The successful development and deployment of the NLP models will further enable the construction of large-scale database to uncover the spatial heterogeneity of climate change impact and decarbonization/electrification status and to identify key opportunities in adaptation and energy decarbonization to inform future decision making. The research lab and the specific project will leverage AL/ML to help improve the sustainability, resilience, and equity of people and the infrastructure and technologies they rely on amidst the growing threat of climate change. The AI/ML models developed in the project will facilitate other researchers to address similar questions for sustainability and equity, while the data and insights gained in the project will inform policy making and capital allocation to develop more equitable, resilient, and decarbonized communities.

Professor Ram Rajagopal will support the postdoc fellow by : (1) hosting workshops and webinars with the involvement of researchers across different disciplines and various stakeholders to foster the community, facilitate the real-world applications of research, and help the postdoctoral fellow build the connections; (2) connecting the postdoctoral fellow with policymakers (e.g., California Public Utilities Commission, DOE) to distill policy implications from the research and delivering widespread impact; (3) giving the postdoc the opportunity to mentor and work with graduate/undergraduate students from different backgrounds, and providing advisory support to help him/her improve the skills of mentoring, teaching, and grant application to make him/her fully prepared for a successful future career in academia.

Stanford HAI is also committed to creating a diverse community of scholars who are engaged in contributing to the understanding and advancement of Human-Centered AI. Postdoctoral fellows will have the opportunity to engage with one another and with the broader Stanford HAI research community. They are also expected to participate in professional development, cohort-building, and other programmatic activities organized by HAI.

Qualifications

We are looking for self-motivated candidates interested in applying AI to predict the impacts of the rapid electrification and decarbonization of the energy system. Successful candidates will have the following:

• PhD in Civil and Environmental Engineering, Computer Science, Electrical Engineering, Mechanical Engineering or a related field
• Experience developing AI for infrastructure detection and adoption analysis at large-scale
• Knowledge of energy systems engineering and policy Track record of publications in leading AI/ML and energy workshops and conferences (e.g. AAAI, Neurips, ICML, ACM e-Energy) and energy related journals (e.g. Nature Energy, Joule, IEEE Power Systems)
• Completion of all doctoral requirements within the last three years and no later than September 1st, 2023.

Evaluation Criteria

• Strong record of peer-reviewed scientific publications (1-2 sample published or working papers)
• Strong oral and written communication skills
• Demonstrated initiative and leadership skills
• Demonstrated experience working on interdisciplinary teams

Timeline

• Application Deadline: March 20, 2023 (Applicants advancing in the review process may be asked to submit additional materials, including letters of recommendation, and may be invited to interview.)
• Selections to be made by mid-April, 2023
• This is a 1-year appointment starting Fall 2023

The expected base pay for this position is $80,000/yr. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the qualifications of the selected candidate, budget availability, and internal equity.

Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.