The ASPIRES internship program is open to all Divisions within Berkeley Lab's Energy Sciences Area. The program will match undergraduate students from CSUEB’s College of Science with scientific and operations staff across ESA divisions for a 10-week paid internship from June 2–Aug. 8, 2025. The 2025 program will be in-person, on-site.
The program sponsors 10 paid interns each summer, so there is no direct cost to divisions or research programs. Project ideas are solicited from prospective mentors in January and February, which can be focused on science research, technology, engineering, or STEM-adjacent fields like science communications. Mentors may be staff in an ESA division or matrixed to an ESA division. If more than 10 project ideas are received, we will down-select based on student interest and fit with the CSUEB intern pool.
Attend a mandatory orientation May 6 to set expectations and ensure a productive experience
Be present and available for the student during the time of the internship
Hold at least weekly one-on-one meetings with the student
Help the student prepare the poster for the end-of-program poster session
Commit to participate in the program requirements
Mentor Information Session: Jan. 27, 10–11 a.m. [sign up]
Project Proposal Submission Deadline: Feb. 21
Mentor Orientation: May 6, 10–11 a.m.
Program Dates: June 2–Aug. 8
Poster Session: Aug. 6
Attend mentor info session. [See slides from mentor info session]
Submit a project proposal (details below). The ASPIRES taskforce reviews each project proposal and may request revisions. Program staff then match students to projects.
Learn by April 21 whether a student was matched to your project.
Attend the mandatory mentor orientation.
Optionally attend the site visit for CSUEB interns .
Program starts June 2.
Use the project submission form to propose a project. The following content is required.
Names and emails for up to four mentors. A lead PI mentor and a primary point-of-contact mentor must be identified.
A project description, to be uploaded in Google Doc format, using the project submission template. Required content includes:
Description of the project that can be easily understood by an undergraduate audience. The project must be appropriate for a 10-week undergraduate project, provide hands-on experience for the intern, and support a research-poster presentation at the end of the program.
Description of the intern’s role.
Description of what the intern can expect to learn.
Description of the research group with link to research website (if applicable)
Link to a short (1-5 minute) video introduction to the project and mentor/research group. This is a critical element of the project proposal. More details can be found in the project submission template and examples can be found in the sample projects below.
Note that the project form may have evolved since the sample projects were submitted. Please adhere to the current submission form, provided above.
Mentor: Tev Kuykendall
Video Interview:
https://drive.google.com/file/d/12BmwiF_8TKkiJUAAuqO6kpbPEBh3RKll/view?usp=sharing
Group description:
In the Inorganic Facility of the Molecular Foundry, we develop methods for synthesis of compound semiconductors resulting in well controlled morphology and electronic structure. This can be achieved through band gap engineering in nanowire heterostructures, or energy level alignment in stacked assemblies of 2D materials. Using gas-phase methods allows us to realize arrays of 1D semiconducting heterostructures as well as approach synthesis of 2D materials with single layer precision. By harvesting the power of chemical vapor deposition and metalorganic chemistry our approach leads to realization of materials with optimized properties or exhibiting exotic behaviors. For example, bandgap engineered systems are a promising platform for the development of unconventional light emitting and energy harvesting devices through control of exciton generation and annihilation.
Division: Molecular Foundry Division
Project description:
Transition metal dichalcogenides (TMDs) are an interesting class of semiconductor materials due to their emergent properties when reduced to thin two-dimensional (2D) layers. While exfoliation and vapor phase growth produce extremely high-quality 2D materials, direct fabrication at wafer scale remains a significant challenge. In previously published results, we demonstrated a method that we call “lateral conversion,” which employs chemical conversion of a metal-oxide film to TMD layers by diffusion of precursor propagating laterally between lithographically defined silica layers, resulting in patterned TMD structures with control over the thickness down to a few layers. The intern will work on further development of this synthetic method. The synthesis has two distinct components: 1) Micro lithography and substrate preparation, and 2) sample annealing and conversion to the resulting TMD. The intern will focus on processing lithographically patterned substrates using chemical vapor deposition (CVD) under a variety of conditions to optimize the growth strategy and control their morphology and crystalline quality. The main goal of the internship is to explore and optimize different synthetic conditions for growing 2D TMD semiconductor films. They will study the effect of precursor conditioning, pressure, temperature, and reactive gasses on the TMD growth. Using a variety of characterization techniques, they will narrow down the process, through successive experiments and characterization, to control size, thickness, and size distribution, producing high-quality TMD materials.
Intern’s role:
The intern will learn how to conduct independent research on solid state materials synthesis.
You will be responsible for synthesizing 2D TMD films using a two-step “lateral conversion” synthesis method.
You will learn how to characterize the samples using a variety of synthetic and analytic techniques.
You will learn how to interpret results, and make improvements to the synthetic process using feedback for successive experiments.
They will receive careful oversight and training during the first month, until they are qualified to work independently. Additional training will be given as needed. Regular discussions will be had to interpret results and gauge their progress.
What can the intern expect to learn?
They will learn a variety of synthetic and analytic techniques, such as:
Chemical vapor deposition (CVD) synthesis
Raman spectroscopy
Optical microscopy
You will learn about the lithographic process and microfabrication techniques
You will be mentored in the creation of a final poster project and will learn how to present their data using written text, plots, photographic images, and illustrations.
Mentor name: Wiebke Koepp (main mentor), Ashley White & Eli Rotenberg (co-mentors)
Video Interview: https://drive.google.com/file/d/1itDu3UL-2y9fFVYhJq9GVNaol0cQ4FoA/view
Group description:
The computing group at the ALS develops software and algorithms to support ALS scientists and users with their needs in regards to data management, processing and analysis. This project is further supported by the communications group, the user office, and scientific staff at the ALS with expertise in
both provided data and underlying science.
Division: Advanced Light Source
Project description:
Our summer intern will perform large-scale data analysis and create visualizations that represent the 30 years of science performed at Berkeley Lab’s Advanced Light Source (ALS).
The ALS is a scientific facility that uses a particle accelerator called a synchrotron to generate bright beams of x-rays and other types of light for scientific experiments across a broad range of fields. (See here and here for video explainers created by 2022 ASPIRES intern Miles Vizinau.) More than 1,800 visiting researchers from around the world use the ALS each year to inform treatments for cancer and COVID, tackle climate change, understand the origins of the solar system, and much more. Over the last 30 years, they’ve published their findings in over 16,000 scientific journal articles.
October 2023 marks the 30th anniversary of “first light,” or the first experiment at the ALS, and the facility will soon undergo an upgrade to greatly expand the possibilities for users coming to the ALS in the next 30 years. In preparation for celebrating this milestone and envisioning new scientific breakthroughs to be made at our upgraded facility, we are interested in reflecting on the history of the science conducted at the ALS thus far and its impact.
Our summer intern will be tasked with deriving insight into how the science and scientific researchers at the ALS have changed by looking into research output from the facility and user demographics. Starting from raw data made up of a set of publications with authors, titles, and publishing information, as well as user data including institution and state/country, the intern will create visualizations that give an overview of the temporal development of the underlying science and people involved. The final visualization(s) will be displayed at the 30-year anniversary celebration and additionally serve as a starting point for discussions at upcoming visioning workshops that look toward the next 30 years of the ALS.
Intern’s role:
The intern will follow all typical steps of the visualization pipeline:
Task specification: Identify a specific data analysis/visualization task in collaboration with the communications and user office teams
Data acquisition: Complement the provided publication/user data set according to selected task
Data enrichment/filtering/transformation: Extract patterns from the collected data, e.g. trends/changes in topics, collaboration compositions, ...
Visual mapping and rendering: Choose an appropriate visualization for the derived insight
Communication: Write accompanying text to highlight the derived insight
What can the intern expect to learn?
Python-based data acquisition, analysis and visualization
Application of additional visualization software for text and graph data
Overview of science topics and techniques researched at a synchrotron light source like the ALS
Steps typically involved in a visualization pipeline - getting from raw data to an image
Introduction to statistics of research output: Bibliometrics and Scientometrics
Mentor name: Kristin A. Persson. Co-mentors Rishabh D. Guha, Alex Epstein
Video Interview:
https://drive.google.com/file/d/1f2vLiWVY9K_-FBPVJ3spI_-EdvLoRnpu/view?usp=sharing
Group description:
We work in the Persson Group supervised by Dr. Kristin Persson. Our group works on multiple connected areas which strives to use computational and statistical tools to understand the chemical and physical behavior of materials - https://perssongroup.lbl.gov
Division: Materials Science Division
Project description:
The accumulation of plastic waste is one of the critical environmental challenges the world is facing. One important route to solving this waste problem is to make plastics that are easy to recycle into new products that are just as good as the original. In our research group, we use simulations on the atomic scale to help design plastics that can be recycled through a chemical process where they are deconstructed in strong acid. One outstanding question in this process is: why do different acids drastically change the rate of recycling? In this project, we will use molecular simulations to study how different acids ionize the plastic at different rates, and their subsequent effects on the overall recycling rate.
Intern’s role:
Prepare molecular dynamics simulations by expanding on existing Python code
Submit and manage simulations on a supercomputing cluster
Statistically analyze molecular dynamics simulations by writing Python code
Meet with co-mentors at least once a week, but feel free to meet as much as needed
Present short (5-10 min) presentations in bi-weekly meetings with our research group
What can the intern expect to learn?
How to implement Python packages into their own scripts for preparing and analyzing molecular dynamics (Pymatgen, SolvationAnalysis, MDAnalysis)
How to run molecular dynamics simulations with OpenMM
How to use the supercomputing facilities at LBNL to design and submit jobs
How to analyze and interpret large amounts of data to form concise conclusions