Job Details

Lawrence Berkeley National Laboratory
  • Position Number: 5061299
  • Location: Berkeley, CA
  • Position Type: Computer and Information Technology

Student Assistant - 101078
Division: SN-Scientific Networking

2024 ESnet Summer Student

Do you have a passion for understanding and developing network systems? Consider ESnet's Research and Development team. ESnet's mission is to accelerate science by delivering unparalleled networking capabilities, tools, and innovations. ESnet interconnects the US National Laboratory system, is widely regarded as a technical pioneer, and is currently the fastest science network in the world. We are a dynamic organization, highly motivated and focused on results. We are working at the leading edge of software-defined networking, network knowledge plane, dynamic network infrastructure, network visualization, network knowledge plane, multi-domain and multi-layer architectures, deep learning, etc. The successful student will be the one who brings strong and diverse coding skills and is very self-motivated.

Project One
Title: Improving Network Observability
Term: Summer 2024
Required skills: Computer Networking familiarity, Python or comparable programming language, Data Collector Tools (Telegraf, Logstash, or comparable), Grafana or similar visualization systems, Elasticsearch or similar NoSQL databases, Machine Learning (optional)
ESnet maintains several tools that collect extensive amounts of data about the state of its network. We store this collected data along with metadata in a database, which is used to drive a wide range of reports, visualizations, and dashboards for use both internally and externally. We are looking to expand the capabilities of these systems to enable new possibilities for our users.

This internship will focus on assisting ESnet developers in exploring and implementing new capabilities in data collection and analysis. Examples include but are not limited to - conversion of SNMP polling to streaming telemetry, machine learning of datasets to discover and alert on anomalies, adapting current ESnet data collection components to open source as part of the newly formed metrANOVA project, and integration of entirely new internal datasets. An effort will be made to align the candidates' interests and capabilities with the project.

Project Two
Title: Network Orchestration with Clixon
Term: Summer 2024
Required skills: Client-Server model concepts and REST APl; Linux / MacOS; git and gitlab; Python, Java, or C
Network Orchestration is a crucial component for Wide Area Network (WAN) providers like ESnet to manage an increased amount of networking devices and enhance the overall robustness of the network. It also decreases the deployment time of new networking equipment and helps significantly to reduce the configuration complexity. This project is about gathering some hands-on experiences in network orchestration by leveraging a combination of different open-source tools. One of the objectives is to develop a very basic workflow to e.g. configure a loopback interface on a router. Another potential outcome of this project would be a feature and implementation comparison with ESnet's pre-existing services which are currently based on a proprietary platform, as well as the required steps and changes to move to an open-source product.

Project Three
Title: ESnet Figma Design System
Term: Summer 2024
Required skills:

  • Visual and communication design principles including typography, iconography, color theory, and grid systems with experience crafting for multiple web platforms.
  • Familiarity with Figma, Sketch, Adobe Creative Suite, or comparable design software.

Desired skills:

  • An understanding of responsive design and how to apply mobile-first responsive strategies in design tooling such as Figma, Sketch, or Adobe Creative Suite.
  • Familiarity with designing, maintaining, and scaling, new components, tokens, and elements in an existing design system.
  • We desire applicants to have a well-structured reviewable portfolio website.
  • Familiarity with accessibility design.

ESnet is developing its design system to be used across its product portfolio. A design system is a pattern for handling the common look and feel of user interfaces at scale. They enable organizations to develop frontend solutions at a higher velocity with a more consistent output. This is a high-visibility project where the designer will have touchpoints with multiple groups across the organization. The end product will have high public visibility.

The scope of this internship is to assist in designing components by delivering low-fidelity wireframes and high-fidelity visual comps. The ideal candidate will also assist with crafting the documentation for these new components and assist with hand off to developers.

Project Four
Title: Streamlining Deployment Workflow for Globus Data Transfer Sites with Ansible
Term: Summer 2024
Required Skills:

  • Familiarity with Virtual Machines and Docker
  • Familiarity with UX/UI Concepts and Design
  • Familiarity with HTML, CSS, and some JavaScript

Desired Skills:

  • Knowledge of Graphic Design concepts
  • Experience in UX/UI Concepts and Design
  • Sense of Humor
  • Visual and communication design principles including typography, iconography with experience crafting
  • Familiarity with the Django framework is a plus

This internship opportunity offers an immersive experience in accelerating scientific progress through optimizing the deployment workflow for a Django-based web application. We’ll leverage Docker, Vagrant, and Ansible to parameterize an existing Django codebase for easy deployment and styling for downstream developers and infrastructure teams. The selected student will gain hands-on experience in transforming an existing code repository into a seamlessly deployable system across various prestigious institutions in the United States and globally.

Intern Responsibilities:

  • Parameterize Codebase for Enhanced Styling
  • Scripting in Ansible for Dockerized and Vagrant Deployments
  • Reduce deployment friction, driving nationwide and global adoption

Project Five
Title: ESnet Scientific Networking Communications & Multimedia Internship
Term: Summer 2024
Required skills:

  • Strong writing and editing skills, preferably experience covering science and/or technology subject matter.
  • Familiarity with Web content management systems (WordPress, Drupal).
  • Proficiency in using video-creation tools to create short, impactful videos (whether simple, such as Canva and iMovie, or advanced, such as Adobe Premiere).
  • Proficiency in editing images using Adobe Photoshop.

The ESnet Communications & Multimedia Internship will offer both an overview of strategic external and internal communications at a cutting-edge scientific networking organization and offer hands-on experience in scientific storytelling for different audiences, through assisting with Web features, press releases, internal newsletters, and social media, and supporting a complex website migration project. The summer intern will also be entrusted with creating several short videos, from “Why I Like Working for ESnet” staff testimonials to explainer-type videos that focus on ESnet’s innovative applied-research products and services.

Project Six:
Title: Network Performance Prediction with Foundation Models
Term: Summer 2024
Required skills:

  • Expert with foundation modeling
  • Willing to learn about network performance prediction
  • Proficient in written communication
  • Proficient in Python programming

A research group led by Professor Arpit Gupta from UCSB has been interacting with ESnet management on exploring topics of automating some routine operations of ESnet. In particular, a summer project has been established to understand the possibility of applying the foundation model the UCSB team has developed for network traffic prediction. This task might provide helpful information for managing expected high volumes of traffic on some backbone links operated by ESnet. The advantage of the foundation models is that they could systematically reduce the dependency on labeled data that are hard to come by for use cases involving unusually large volumes of traffic on a link. The UCSB team has demonstrated successful use cases involving a campus network, we are interested in validating the approach for network backbone operated by ESnet.

Project Seven
Title: Machine Learning for Identification of REE-CM Hot Zones
Term: Summer 2024
Required skills:

  • Expert with machine learning tools
  • Willing to learn about classification problems involving gamma-ray detection data
  • Proficient in written communication
  • Proficient in Python programming

Characterization of Rare Earth Elements and Critical Minerals (REE-CM) in unconventional and secondary sources is a complex task that needs to overcome the challenges of detecting low and variable concentrations and the uniqueness of every source material deposit in terms of composition, host material, and disposal environment. Like in traditional mineral prospecting, delineation of the REE-CM “hot zones” is critical for assessing the economic viability of these sources. Here, a hot zone is defined as a spatially delineated volume of high REE-CM concentrations within the tailing deposits.

We propose a machine learning (ML)-aided multi-physics approach for rapid identification and characterization of REE-CM hot zones in mine tailings for efficient recovery with a focus on coal and sulfide mine tailings and other processing or utilization byproducts, such as fly ash and refuse deposits.

This proposed multi-physics approach integrates a range of advanced and novel geophysical, radiological, and optical technologies deployed on aerial and surface platforms suitable for REE-CM prospecting. Aided by multiple existing and emerging core and lab analytical technologies, this integrated approach provides a cross-scale capability from whole tailing REE-CM hot zone identification down to mineralogical and REE-CM characterization and quantification. Advanced ML capabilities are key to integrating these multi-physics datasets for identifying hot zones and optimizing sensing technology deployment. Feature engineering ML joined with federated learning and transfer learning will be used for data organization, feature extraction, and privacy protection.

As part of the application process, please submit a statement explaining which project you prefer to apply to.


  • The Summer 2024 Term is 12 weeks (6/3/2024 - 8/26/2024). Student participation requires 40 hours per week for Summer appointments. A "late start" date can be considered for academic reasons.
  • The student assistant appointment can be renewed based on satisfactory job performance, continuing availability of funds, and ongoing operational needs.
  • Students who participate in their school's Co-Op programs can also apply.
  • Salary will be predetermined based on student step rates.
  • This position may be subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
  • Work will be primarily performed at Lawrence Berkeley National Lab in Berkeley, CA, or the Champaign, Illinois office, with the possibility of working remotely.

How To Apply
Apply directly online
and follow the on-line instructions to complete the application process.

Berkeley Lab is committed to Inclusion, Diversity, Equity and Accountability (IDEA) and strives to continue building community with these shared values and commitments. Berkeley Lab is an Equal Opportunity and Affirmative Action Employer. We heartily welcome applications from women, minorities, veterans, and all who would contribute to the Lab's mission of leading scientific discovery, inclusion, and professionalism. In support of our diverse global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.

Equal Opportunity and IDEA Information Links:
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