EESA Research Projects Summer 2025

This summer project will tackle detecting and measuring trace gas emissions from various sources with cutting-edge measurement tools and modeling techniques to track emissions, pinpoint their origins, and assess their impact. Along the way, the intern will gain hands-on experience in instrumentation, data analysis, and modeling while learning essential skills in Python programming and LaTeX for scientific writing. 

Desired Education Level: Community college student, Undergraduate student, Masters student, PhD student

Relevant Internship Programs: SULI, CCI, MLEF, GEM, DOE Science Graduate Student Research (SCGSR)

Mentor: Andre Santos (ALDSantos@lbl.gov) 

We have a variety of mobile and stationary systems for measuring greenhouse gases in the atmosphere.  We will work with the student to choose a project involving collection of new data and/or working with existing data to investigate greenhouse gas dynamics related to human-caused and biological sources and sinks.  The intern will learn how trace gases are measured and calibrated, and then used to detect processes affecting their atmospheric concentrations and climate impacts.  

Desired Education Level: Community college student, Undergraduate student

Relevant Internship Programs: CCI, SULI

Mentor: Andrew Moyes (ABMoyes@lbl.gov) 

Tropical forests play a crucial role in the global carbon, water, and energy cycles, so understanding how this biome will respond to climate change is necessary for accurately predicting Earth system trajectories under different climate change scenarios. The Next Generation Ecosystem Experiments (NGEE) Tropics team has developed a sophisticated vegetation model to study tropical forests, but we need to test the model and benchmark simulations with as much data as possible. In this summer project, you will work on synthesizing data collected in field campaigns to aid in model benchmarking. You will deepen your knowledge of tropical forest ecology and sharpen your skills in data analysis while helping to advance our understanding of how tropical forests will respond to climate change. You will also have the opportunity to learn about vegetation demography modeling. This internship would be ideal for someone interested in Earth science and/or data science. We are especially interested in Community College students.  

Desired Education Level: Visiting Faculty member, Community college student, Undergraduate student

Relevant Internship Programs: VFP, CCI, SULI

Mentors: Charlie Koven (CDKoven@lbl.gov) and Jessie Needham (JFNeedham@lbl.gov)

This project will focus on developing best estimates of snow albedo and snow temperature from satellite and field instruments, along with machine learning, to understand how snow and water resources are changing in the Upper Colorado River Basin.  These estimates are critical for understanding how and when snow accumulates, persists, and melts, and how those processes are changing, at the source of water resources for 40 million people. The summer researcher will learn how to use data from DOE's Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign and NASA and ESA satellites as well as hone their machine learning skills.

Desired Education Level: Undergraduate student, Masters student, PhD student

Relevant Internship Programs: SULI, DOE Science Graduate Student Research (SCGSR) 

Mentors: Daniel Feldman (DRFeldman@lbl.gov)      

Fungi play a key role in the decomposition of dead wood, a globally significant carbon stock that contributes to soil formation and nutrient cycling at the ecosystem scale. Research has shown that the composition of fungal communities influences wood decay rates as much as, if not more than, local climate conditions. However, fungi vary widely in their ability to decompose wood, leading to substantial differences in decomposition rates across fungal communities. This project builds on recent advancements in multi-scale fungal agent-based modeling (ABM) and the integration of machine learning (ML) with ABM to enable adaptive, cross-scale modeling and emulation. Specifically, the goal is to develop a fast-running proxy for the fungal ABM that can be used for Bayesian calibration. The primary approach will involve modeling ABM outputs as a dynamical system. To address potential challenges, generative adversarial networks (GANs) will be explored as a risk mitigation strategy. Additionally, the use of Large Language Models (LLMs) will be investigated to create surrogates that capture the statistical variability inherent in large ensemble runs of stochastic ABM models. 

This work will provide training in computational techniques for emulating mechanism-based biological models on high-performance GPU architectures, ultimately aiming to determine the optimal level of detail needed to advance a trait-based understanding of wood decomposition by fungi. 

Desired Education Level: Community college student, Undergraduate student, Masters student, PhD student

Relevant Internship Programs: CCI, GEM, SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Gianna Marschmann (GLMarschmann@lbl.gov)  

Tropical forests play a crucial role in the global carbon, water, and energy cycles, so understanding how this biome will respond to climate change is necessary for accurately predicting Earth system trajectories under different climate change scenarios. The Next Generation Ecosystem Experiments (NGEE) Tropics team has developed a sophisticated vegetation model to study tropical forests, but we need to test the model and benchmark simulations with as much data as possible. In this summer project, you will work on synthesizing data collected in field campaigns to aid in model benchmarking. You will deepen your knowledge of tropical forest ecology and sharpen your skills in data analysis while helping to advance our understanding of how tropical forests will respond to climate change. You will also have the opportunity to learn about vegetation demography modeling. This internship would be ideal for a student interested in Earth science and/or data science.

Desired Education Level: Community college student

Relevant Internship Programs: CCI

Mentors: Jennifer Kowalczyk (JenniferKowalczyk@lbl.gov)  

This summer project focuses on enhancing the E3SM Land Model (ELM) to better simulate pan-Arctic ecosystems. The pan-Arctic holds vast carbon stocks, and accurately modeling how permafrost, hydrology, and vegetation interact is key to understanding future climate change. You'll work with scientists from the Next-Generation Ecosystem Experiments -  Arctic (NGEE-Arctic) project on resolving sub-grid landscape heterogeneity in hydrology, vegetation, and soils, helping to improve predictions of pan-Arctic ecosystem dynamics in a warming world. You’ll gain skills in earth system modeling, data analysis, and understanding complex climate feedback.

Desired Education Level: Undergraduate student

Relevant Internship Programs: SULI

Mentors: Jing Tao (JingTao@lbl.gov)   

The intern will join the interdisciplinary team of the ESS-DIVE data archive, which consist of earth/environmental scientists, and computer/data scientists. The intern will be involved in data management and informatics research activities, including use of data curation tools and workflows to improve the access and usability of the DOE’s environmental datasets. This will include building and using automated tools to improve dataset metadata and ensure that data files follow standard formats, making them more discoverable and usable.

Desired Education Level: Community college student

Relevant Internship Programs: SULI, CCI

Mentors: Joan Damerow (JoanDamerow@lbl.gov)   

This summer project explores how trees breathe and use water, helping us understand their role in our changing climate. The goal is to measure tree respiration and water use in real time, using cool tools like sensors and data loggers. By doing this, you'll learn how trees respond to different environmental factors and contribute to ecosystems. You'll gain hands-on experience in environmental monitoring, data analysis, and gain skills that are valuable in research and sustainability careers!

Desired Education Level: Visiting Faculty member, Community college student, Undergraduate student, Masters student, PhD student

Relevant Internship Programs: VFP, CCI, SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Kolby Jardine (kjjardine@lbl.gov)    

This work focuses on leveraging advanced remote sensing technologies to detect and quantify natural disturbances, such as windthrows, fires, and droughts, across tropical forests globally. By integrating high-resolution satellite imagery and time-series data, we aim to assess the spatial and temporal patterns of disturbances, their drivers, and their impacts on forest structure, function, and carbon dynamics.

Desired Education Level: Visiting Faculty member, Community college student, Undergraduate student, Masters student, PhD student

Relevant Internship Programs: VFP, SULI, CCI, DOE Science Graduate Student Research (SCGSR)

Mentors: Robinson Negron-Juarez (robinson.inj@lbl.gov) 

This summer internship will investigate one or more mechanistic modeling hypotheses related to nutrients dynamics, light scattering or photosynthesis, that are used in terrestrial biosphere models (TBMs).  The overall goal is to learn about why these hypotheses are effective, and to probe the mathematical relationships between the inputs and the outputs to these specific hypotheses. Potential skills learned will be 1) running the FATES TBM and 2) writing python and/or fortran code vignettes in a notebook (Jupyter/colab) setting. 

Desired Education Level: Community college student, Undergraduate student, Masters student, PhD student

Relevant Internship Programs: CCI, SULI, DOE Science Graduate Student Research (SCGSR)

Mentor: Ryan Knox (RGKnox@lbl.gov)  

Enhanced weathering (EW) through application of ground rock is a competitive carbon removal strategy that combines both capture and storage. Studies suggest that using EW in agricultural fields has the potential to remove 0.5-2 gigatons a year by 2050. This approach has several potential co-benefits, as well, such as higher crop yields, reduced fertilizer or liming needs, and enhanced soil health. In order to maximize the potential of EW in agricultural fields, it is necessary to understand the magnitude of realistically achievable rate enhancement under different interacting abiotic-biotic processes and the climatic interactions that control these rates. The project will explore conditions and processes under which weathering rates are truly enhanced.

Desired Education Level: Visiting Faculty member, Masters student, PhD student

Relevant Internship Programs: VFP, SULI, MLEF, DOE Science Graduate Student Research (SCGSR)

Mentor: Bhavna Arora (barora@lbl.gov) 

The CI-D Experiment is a field test performed at the Mont Terri rock laboratory (Switzerland) to understand the migration of radionuclides in clay formations. In this project, we will extend our efforts on the CI-D experiment by taking a broader view of radionuclide migration. We will continue to focus on anions, which are arguably the biggest risk drivers for radionuclide migration from deep geological storage of nuclear waste. We will work with the project intern to further this effort, beginning with a publication of the CI-D modeling undertaken with Carl Steefel and Christophe Tournassat.

Desired Education Level: PhD student 

Relevant Internship Programs: Ingenuity Program

Mentor: Carl Steefel (CISteefel@lbl.gov)  

Bentonite pellet/powder mixtures have been applied as an Engineered Barrier System for high level radioactive waste underground repository. The two sets of bench-scale column tests at LBNL using bentonite powder vs. pellet/powder mixture have shown considerably different hydration and swelling processes. The observed faster hydration in the pellet/powder bentonite mixture indicates the impacts of heterogeneity and potential fast flow path developed during hydration. In this project, you will be investigating the heterogeneous hydration in bentonite pellet/power mixtures, by preparing column samples (5 cm diameter by 10 cm long), conducting experiments, and assisting with image data acquisition and analysis. You will be introduced to the fascinating X-ray CT scanner housed at LBNL, and work on CT images obtained for scientific research, rather than medical diagnosis you may have seen. Supported by LBNL scientists, you will also have the opportunities to access the science behind these images, dig deeper using dedicated software and develop your own code to help interpretations.

Desired Education Level: PhD student

Relevant Internship Programs: Ingenuity Program

Mentor: Chun Chang  (ChunChang@lbl.gov)  

Geologic hydrogen is a new and emerging field that offers real potential for mitigating climate change. For this summer project, we are looking for a highly skilled and motivated PhD student to conduct numerical modeling of reservoir-scale hydrogen gas flow and geomechanics for deep geologic hydrogen extraction. The student will engage at the frontiers of research of geologic hydrogen with a multidisciplinary team of computational geoscientists, field geologists and experimentalists.

Desired Education Level: PhD student

Relevant Internship Programs: SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Jonny Rutqvist (jrutqvist@lbl.gov)

Gas flow through the Earth is critically important in subsurface engineering activities, such as nuclear waste disposal, carbon sequestration and hydrogen storage. Subsurface gas migration involves complex processes, including multiphase fluid flow with gas breakthrough, phase change expansion, pressure buildup, as well as potential gas fracturing with rapid gas release that could potentially be catastrophic. In this project, we look for a skilled student to conduct numerical simulations of deep subsurface gas flow in various types of host rocks. A detailed program will be designed together with the candidate depending on the candidate’s background and interest. 

Desired Education Level: PhD student

Relevant Internship Programs: Ingenuity Program

Mentors: Jonny Rutqvist (jrutqvist@lbl.gov)

Geologic hydrogen is a new and emerging field that offers real potential for mitigating climate change. For this summer project, we are looking for a highly skilled and motivated PhD student to conduct multi-phase flow modeling for the optimized design of cyclic injection for geologic hydrogen extraction. The student will be provided an excellent opportunity to engage at the frontiers of research of geologic hydrogen, while learning alongside a multidisciplinary team that includes computational geoscientists, field geologists and experimentalists. 

Desired Education Level: PhD student

Relevant Internship Programs: SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Mengsu Hu (mengsuhu@lbl.gov) 

Machine learning (ML) can be a practical toolset to predict a range of multi-physics behavior in the subsurface energy geosciences. Previously, we used machine learning to identify multiscale geological features from Mt Terri URL (a 2022 Ingenuity project), to predict fault slip behavior induced by fluid injection in an experiment conducted in Mt Terri URL, and to predict wastewater injection induced seismic events in space and time in Oklahoma at the basin scale (an LBNL-CSUEB Intern project). We also made a first attempt to predict microearthquakes from hydraulic stimulations conducted at the EGS Collab (a 2024 Ingenuity project). In this project, we look for a skilled student to carry out machine learning analysis for understanding and predicting fractures and fault behavior from meso (m) to repository (km) scale. The goal is to quantify the fault and fracture responses to engineering loading such as hydraulic loading in geothermal reservoirs and thermal loading in nuclear waste repositories.  Detailed technical goals will be set based on the background and interest of the successful candidate.

Desired Education Level: PhD student

Relevant Internship Programs: Ingenuity Program

Mentors: Mengsu Hu (mengsuhu@lbl.gov) 

Tens of millions of gallons of water is used per well in hydraulic fracturing (HF) operations to produce gas and heat from tight reservoirs. About three-quarter of the injected water is permanently lost and restricts counter-current flow of gas back to the wells. Cost of water supply and flow-back water treatment can be astronomical depending on the number of wells. This project aims to leverage data availability with machine learning approaches to quantify HF water loss and explore strategies for mitigating excessive losses that could impede production. The ideal student candidate for this project is: (1) skilled in numerical simulations and machine learning methodologies for deep subsurface flow processes; (2) highly motivated to complete assigned tasks; and (3) a high achiever with track record of technical presentations and/or publications. A detailed work plan will be developed with the selected candidate depending on the candidate’s background and interest.

Desired Education Level: Masters or PhD student

Relevant Internship Programs: Mickey Leland Energy Fellowship (MLEF), DOE Science Graduate Student Research (SCGSR)

Mentor: Omotayo Omosebi (OAOmosebi@lbl.gov) 

Cement is used as a liner in the emplacement tunnel of the geological repository for high-level radioactive waste. The interaction between cement and bentonite has a profound impact on the repository's long-term safety. In this project, the intern will conduct a reactive transport model with the mentors to study the alteration of bentonite and concrete. If time permits, migration of radionuclides can be added to the model to study the transport of radionuclides through bentonite and cement.  The intern will learn the geochemistry of cement and bentonite and LBNL in-house reactive transport code, TOUGHREACT, and contribute to the technical report and journal articles.

Desired Education Level: Masters or PhD student

Relevant Internship Programs: Ingenuity Program

Mentors: Omotayo Omosebi (OAOmosebi@lbl.gov) & Liange Zheng (lzheng@lbl.gov)

The project will expose interns to first principle calculations of the free energies of reactions in the gas and solution phases. We will use a range of quantum chemistry methods ranging from the density functional theory to post-Hartree-Fock methods. The intern will learn state-of-the-art computational tools and apply them to solve urgent environmental problems.

Desired Education Level: Community College student, Undergraduate student, Masters or PhD student

Relevant Internship Programs: Ingenuity Program

Mentor: Piotr Zarzycki (ppzarzycki@lbl.gov) 

The intern will use machine learning algorithms to identify hidden patterns in the dynamics of the complex environmental fluid (brines, spent nuclear fuel, wastewaters). We will examine the evolution of solutions at the molecular scale  - simulation trajectories - looking for the couplings and correlations between distant ions and molecules. The intern will learn how to apply ML/AI methods to high-precision molecular dynamics simulations to unsurface hidden and potentially unexpected relationships, with implications for improving our understanding of brines, spent nuclear fuel, and wastewater. 

Desired Education Level: High school student

Relevant Internship Programs: Berkeley Lab K-12 Program

Mentor: Piotr Zarzycki (ppzarzycki@lbl.gov) 

Software is increasingly becoming interoperable such that we can mix and match components to rapidly develop new models. In this project, we aim at developing and/or demonstrating software interfaces that enable this interoperability for geochemical models. The products of this work will be used by researchers worldwide to develop the next generation of multiphysics simulators. This project provides an opportunity to hone your Python programming as well as your scientific communication skills.

Desired Education Level: Undergraduate student, Masters student

Relevant Internship Programs: SULI, CCI

Mentor: Sergi Molins (smolins@lbl.gov)  

Geologic hydrogen is a new and emerging field that offers real potential for mitigating climate change. For this summer project, we are looking for a highly skilled and motivated PhD student to conduct laboratory serpentinization experiments for developing optimal geologic hydrogen extraction. The student will be provided with an excellent opportunity to engage at the frontiers of research of geologic hydrogen, with a multidisciplinary team of experimentalists, field geologists, and computational geoscientists.

Desired Education Level: PhD student 

Relevant Internship Programs: SULI, DOE Science Graduate Student Research (SCGSR)

Mentors: Wenming Dong  (WenmingDong@lbl.gov) and Carl Steefel (CISteefel@lbl.gov)

Clayey materials are important in engineered barrier systems (EBS) for potential U.S. DOE nuclear waste repository sites. For this summer project, we are looking for a highly skilled and motivated student to conduct laboratory experiments to determine how water chemistry influences swelling pressure, microstructural and ion transport in compacted clay system. The student will be provided with an excellent opportunity to engage in the frontiers of research of geologic nuclear waste disposal, with a multidisciplinary team of geochemists, hydrologists, and computational geoscientists.

Desired Education Level: Undergraduate student, Masters or PhD student

Relevant Internship Programs: Ingenuity Program 

Mentor: Wenming Dong (WenmingDong@lbl.gov)