Postdoctoral Fellow in Environmental Studies: Clean Energy Policy and Generative

The Wilson group in the Department of Environmental Studies (ENVS) at Dartmouth College invites applications for a postdoctoral fellow to join an interdisciplinary team developing "expert in the loop" research workflows that combine generative AI (GenAI), domain expertise, and qualitative methods to address information overload and emerging institutional risks in energy policy and governance.

This position offers the opportunity to develop and test new methods and tackle critical challenges in clean energy transitions. It is partially supported by grants connecting social science, computer science, and energy-policy expertise. The fellow will create and evaluate approaches that use large language models (LLMs) to scale qualitative analysis of policy, regulatory, legal, and stakeholder documents in replicable ways. These methods will be applied to contested clean energy areas such as offshore wind, transmission, and battery storage.

The fellow will be advised by Professor Elizabeth J. Wilson, working closely with Simon Stone from Dartmouth Research Computing and partners at DTU Wind in Denmark. The team emphasizes open, collaborative, and actionable research on real world energy challenges.

Dartmouth is a research-intensive Ivy League university with graduate programs in the sciences, engineering, medicine, and business. Postdoctoral scholars are supported by the Guarini School for Graduate and Advanced Studies, including their community initiatives. Dartmouth as a whole is committed to academic excellence and encourages the open exchange of ideas within a culture of mutual respect. Dartmouth welcomes people with different backgrounds, life experiences, and perspectives and believes that diversity in all its forms enhances academic excellence. In addition, we value applicants who have a demonstrated ability to contribute to Dartmouth's initiatives in undergraduate research, such as Early Research Access in the Sciences, the EE Just Program, and the Academic Summer Undergraduate Research Experience. Applicants should address in their materials how their research, teaching, service, and/or life experiences prepare them to serve Dartmouth's commitment to academic excellence in an environment that is welcoming to all.

Major Duties/Responsibilities

• Conduct research at the intersection of energy governance and computational social science, focusing on LLM enabled qualitative analysis.
• Design and test GenAI workflows for tasks such as corpus building, document review, coding, and synthesis.
• Analyze large scale text corpora (e.g., policy filings, environmental reviews, stakeholder comments, news).
• Collaborate with an interdisciplinary team; contribute open, reproducible research outputs (code, datasets).
• Publish in peer reviewed journals and present at conferences.
• Mentor and coordinate Dartmouth student researchers.
• (Optional) Co-teach up to one course per year on energy and environment topics.

Research Themes

• Governance and institutions in clean energy transitions.
• Energy conflict and discourse: combining LLMs and qualitative methods to analyze public comments, press releases, and media.
• Method development: reliability, bias assessment, corpus construction, and human-AI collaboration protocols.

Salary & Benefits

This position is full-time, non-remote, in-residence at Dartmouth in Hanover, NH, with start date as early as January, 2026. The initial appointment will be for one year, with possibility of renewal pending funding and performance. Salary is competitive and commensurate with experience, following Dartmouth postdoctoral guidelines. Dartmouth offers a comprehensive benefits package for postdoctoral scholars.

Required

• Ph.D. in a relevant field (e.g., computer science, data science, geography, quantitative social science), or ABD with degree received by the start date.
• Demonstrated experience applying LLMs/GenAI in research or practice, such as retrieval-augmented generation (RAG), function calling, and agent orchestration.
• Proficiency in Python or R and familiarity with coding, using computer clusters, and Linux servers
• Strong analytical, writing, and communication skills.
• Interest in stakeholder-engaged, transdisciplinary research.

Preferred

• Research experience in energy policy/governance and qualitative methods.
• Experience with natural language processing (NLP) and data science methods, such as multimodal vector embeddings, state-of-the-art similarity search, and end-to-end retrieval pipelines.
• Experience with hierarchical/statistical models.
• Evidence of research success (publications, preprints, or open research products).

Applicants are encouraged to apply even if they do not meet every qualification, as training can be provided for certain areas of responsibility.

Please submit the following materials via Interfolio:

1. Cover letter, addressing all relevant aspects of the position description including experience with LLMs/NLP.
2. Curriculum vitae, including names and contact information for three references.
3. Research statement (max 1 page).
4. Two representative publications or writing samples.

Review of applications will begin on November 7, 2025. Applications submitted after this date will be reviewed until the position is filled. Recommendations letters will be requested for finalists only. For questions about the position, please contact Prof. Elizabeth J. Wilson at elizabeth.j.wilson@dartmouth.edu, with "Clean Energy Policy and Generative AI postdoc" in the subject line.