Climate AI Student Researcher

Description

Position Title: Climate AI Student Researcher

Requisition : REQ-3890

Position Type: Full time

Together Shaping the Future of Energy

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.

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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, for example:

• Evaluation of Extreme Weather
  :
  Assess the ability of modern climate products and down scaled datasets to represent extremes such as lightning, wind gusts, and hail 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.
• Advanced Return Level Analysis
  :
  Research robust statistical and machine learning methods to estimate the frequency and intensity of rare, high‑impact weather events.
• Renewable Energy Forecast Validation
  :
  Assess the skill of AI‑driven wind and solar predictions against traditional numerical weather prediction (nwp) benchmarks.
• Natural Language Data Interface
  :
  Restructure Python modules to integrate Large Language Models (LLMs), allowing non‑technical end‑users to query complex datasets using natural language.

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 at the intersection of 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.

The role is fully remote, and there is some flexibility in defining the internship timeframe and work schedule. Preference will be given to candidates able to commit to a minimum target of mostly full‑time schedule for 3‑4 months. Hours can be arranged around coursework or teaching loads, ranging from 15 hours (part‑time) to 40 hours (full‑time) per week.

• PhD candidate in meteorology, climate sciences,…