Climate AI Student Researcher

Position Title

Climate AI Student Researcher

REQ-3890

Full time

EPRI provides thought leadership, industry expertise, and collaborative value to help the electricity sector identify issues, technology gaps, and broader needs that can be addressed through effective research and development programs for the benefit of society.

Climate AI Student Researcher

Remote/Home Based

Join a team working at the intersection of climate science, data science, and energy system resilience.

The Energy Systems and Climate Analysis Group at EPRI () is a multidisciplinary team of leading earth scientists, climate economists and energy system analysts. We conduct research to inform real‑world decision‑making, helping electric companies and government partners understand how climate hazards are evolving and what they mean for the future of energy. This internship offers you the opportunity to apply AI/ML methods to an established research portfolio on climate risk and resilience, including efforts to characterize current and projected trends in relevant weather and climate variables, identify potential climate impacts, and conduct quantitative climate risk assessments.

This role is designed for a PhD candidate with experience applying AI/ML methods to earth science research topics, demonstrated quantitative analysis and coding skills, particularly in Python, and familiarity with historical meteorological and/or projected hydroclimate datasets. Familiarity with architecting neural networks, implementing dimensionality reduction, and utilizing modern AI frameworks (e.g., PyTorch or Tensor Flow) to handle high-dimensional geospatial data is a plus.

The ideal candidate will have experience orchestrating large‑scale computational workflows on distributed cloud infrastructure and optimizing model performance via GPU-accelerated computing (e.g., CUDA, CuPy).

Throughout the internship, you will work closely with project leads to translate data-driven findings into research outcomes and communications for end‑users. The successful candidate may work on 2‑3 specific projects including examples such as:

• Evaluation of Extreme Weather like lightning, wind gusts, and hail. Assess the ability of modern climate products and down scaled datasets to represent these extremes to help identify systematic biases and improve the reliability of risk assessments for critical utility infrastructure.
• Compound Hazard Emulation
  :
  Develop AI/ML surrogate models to simulate the interaction of multiple concurrent climate drivers, such as extreme heat and humidity. These emulators provide a computationally efficient alternative to traditional physics-based simulations for long-term planning.
• Advanced Return Level Analysis
  :
  Research robust statistical and machine learning methods to estimate the frequency and intensity of rare, high-impact weather events. This work is critical for updating engineering standards and hardening the grid against low probability occurrences.
• Renewable Energy Forecast Validation
  :
  Assess the skill of AI-driven wind and solar predictions against traditional numerical weather prediction (NWP) benchmarks. You will quantify the added value of these models in reducing uncertainty for energy resource adequacy studies.
• Natural Language Data Interface
  :
  Restructure Python modules to integrate Large Language Models (LLMs), allowing non-technical end-users of weather/climate data to query complex datasets using intuitive natural language. This project bridges the gap between high-level climate science and actionable operational insights.

What makes the ESCA team unique:

You will join a collaborative research environment and work side-by-side with established experts to tackle some of today's most pressing societal challenges energy and climate change in collaboration with electric companies and government entities. We prioritize scientific rigor, data-driven insights, and real‑world relevance. Our interns get hands‑on experience contributing to major research initiatives and often publish journal articles.

Schedule & Format

The role is fully remote and there is some…