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Inside Higher Ed · 4 weeks ago

Post-Doc Research Associate - AI for Credible Climate Mitigation

Chapel Hill, NC

Full-time

Hybrid

Mid, Senior Level

$68K/yr - $72K/yr

The University of North Carolina at Chapel Hill is a global leader in innovative teaching, research, and public service. They are seeking a Postdoctoral Research Associate to join the Data-Driven EnviroLab, focusing on the intersection of AI and climate action, particularly in developing systems for climate mitigation intelligence and ensuring the integrity of climate commitments.

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Responsibilities

Develop, adapt, and evaluate LLM/genAI systems for climate mitigation intelligence, especially for tracking and assessing commitments and actions by cities, regions, and non-state actors

Design and validate benchmarks, metrics, and auditing pipelines that test the accuracy, credibility, fairness, and robustness of LLM outputs related to climate targets, disclosures, and policy claims

Rigorously test genAI/ML models against misinformation, disinformation, and greenwashing, including stress-testing models under adversarial, low-resource, and multilingual settings

Study how generative AI shapes real-world climate behavior, including impacts on corporate and subnational mitigation planning, transparency, and accountability

Contribute to CLAIM’s broader mission: data collection, methodological innovation, open tools, papers, and policy-facing outputs that improve climate commitment integrity

Qualification

LLM trainingAI evaluationPython programmingClimate change knowledgeInterdisciplinary collaborationTime management

Required

PhD in Computer Science, Computing, Statistics or Data Science, or related disciplines

Demonstrated hands-on experience training, fine-tuning, or adapting LLMs / generative AI models (e.g., instruction tuning, domain adaptation, RAG pipelines), with evidence in publications, open-source code, or deployed tools

Strong experience in LLM/genAI evaluation and benchmarking, including methods for factuality/hallucination testing, robustness, calibration, bias/fairness, and/or adversarial stress-testing

Solid programming and ML/NLP engineering skills in Python and ideally modern deep-learning stacks (e.g., PyTorch/JAX, HuggingFace/Transformers, vector databases, agentic/RAG tooling), with ability to build reproducible research pipelines

Working knowledge of climate change mitigation and environmental governance, including familiarity with climate commitments, emissions accounting, or policy/action tracking, and motivation to apply AI in this space

Able to think critically, proactively and work both independently and in an interdisciplinary team environment