[ES_JOBS_NET] Climate AI Student Researcher, EPRI

[Christine Wiedinmyer]

https://vhr-epri.wd1.myworkdayjobs.com/epricareers/job/RemoteHome-Based/Climate-AI-Student-Researcher_REQ-3890

Climate AI Student Researcher

Job Title:Climate AI Student Researcher

Location:Remote/Home Based

Job Summary and Description:

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 (http://esca.epri.com)
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 TensorFlow)
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 downscaled 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 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 duration. Potential for internship to begin before or extend
beyond the typical summer period; in such cases, hours can be arranged
around coursework or teaching loads, ranging from 15 hours (part-time) to
40 hours (full-time) per week.

*Internship Qualifications:*

   -

   PhD candidate in meteorology, climate sciences, geography, earth
   sciences, environmental engineering, energy resources, computer science, or
   a related field; preference for current PhD students.
   -

   Excellent Python skills
   -

   Technical aptitude and experience with quantitative methods and data
   analyses.
   -

   Familiarity with AI/ML methods and models, including deep learning,
   probabilistic modeling, and generative architectures
   -

   Experience collecting and analyzing weather and climate data (weather
   and climate model outputs, surface station observations, reanalysis
   products, satellite observations, etc.)
   -

   Familiarity with the published literature and policies relating to
   climate change impacts, adaptation, and vulnerability (e.g., IPCC WGII
   domain), climate scenarios and projected changes, and
   climate-energy-economics is a plus.
   -

   Excellent research and writing skills; strong oral communications
   skills; ability to present complex information in a clear and concise
   manner.
   -

   A positive attitude, comfortable working both collaboratively and
   independently.

The hourly rate range for Student positions are:

   -

   Undergraduate: $16-29 per hour
   -

   Masters: $27-33 per hour
   -

   Ph.D: $31-36 per hour

These ranges are an estimate, and the actual hourly rate may vary based on
various factors, including without limitation applicant's education,
experience, skills, and abilities, as well as internal equity and alignment
with market data. The hourly rate may also be adjusted based on applicant's
geographic location.

As an EPRI Student, you will not participate in EPRI’s Benefit Programs
which includes health insurance, retirement benefits, vacation, sick leave
(except as set required by law) and holiday pay. However, as a Student
employee you are eligible for the benefits of Social Security, State
Disability Insurance, and Workers’ Compensation Insurance.

For Student positions which require one to relocate to an EPRI office.
Relocation assistance is not provided and the student will be responsible
for covering all relocation costs/expenses.

EPRI participates in E-Verify, an online system operated jointly by the
Department of Homeland Security and the Social Security Administration
(SSA). EPRI uses the system to check the work status of new hires by
comparing information from the employee's I-9 form against SSA and
Department of Homeland Security databases.

EPRI is an equal opportunity employer. EEO/AA/M/F/VETS/Disabled

Together . . . Shaping the Future of Energy.

www.epri.com
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