Scientific Networking Summer Student Assistant

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 working at the leading edge of software-defined networking, network knowledge plane, dynamic network infrastructure, network visualization, multi-domain and multi-layer architectures, deep learning, etc. Opportunities exist in the organization to support: Research, Software, IT Technology, Data analysis, Financial, Operational Improvements, Project Management Tools, Reports and Communications etc.

To learn more about ESnet's internship program, visit https://www.es.net/about/careers/student-internships/

ESnet is hiring Student Assistants for the following projects. Once projects are filled, we will note them as CLOSED. As part of the application process, please submit a statement listing which 1-3 available projects you are interested in and why. You may be contacted by more than one project team. All projects are for Summer 2025 and are hybrid onsite/remote unless noted otherwise.

Project 1: Linux CLAT

Required Skills: CS, Computer networks, Linux

Abstract:

Linux has limited options surrounding automatic transition to IPv6-only operation. The most common transition mechanism on other computing platforms is called 464XLAT, and is defined by RFC6877. The mechanism in which a host implements their portion of this toolset is called CLAT. The student will work on:

- Survey Linux CLAT options

- Test existing implementations, document usability, supportability

- Document any limitations

- Create a set of performance tests comparing against Windows 11 and MacOS CLAT implementations

- Outline potential options for improvement of existing CLAT implementations under linux

- Create an academic paper for publishing results of study

Project 2: Equipment and Topology Discovery - CLOSED

Project 3: SENSE Multi-Domain Network Automation, Application Workflow and Performance Evaluation

Term: Spring/Summer 2025

Required Skills: Computer systems, networks, Linux, Python, Java/JavaScript

Abstract:

The SENSE project (http://sense.es.net) is building smart network services to accelerate scientific discovery in the era of big data driven by Exascale, cloud computing, machine learning and AI. The project's architecture, models, and demonstrated prototype define the mechanisms needed to dynamically build end-to-end virtual guaranteed networks across administrative domains, with no manual intervention. The student will work on:

- Python API client and application workflow automation

- SENSE Orchestrator Web Portal UI and monitoring data dashboards

- Network and data movement performance measurement and analysis (graduate student preferred)

Project 4: Application of AI/ML techniques - CLOSED

Project 5: Reed Solomon Forward Error Correction - CLOSED

Project 6: Machine Learning for Identification of REE-CM Hot Zones

Required Skills: Machine Learning

Abstract:

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. 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 multi-physics approach integrates a range of geophysical, radiological, and optical technologies deployed on aerial and surface platforms suitable for REE-CM prospecting. This approach provides a cross scale capability from whole tailing REE-CM hot zone identification to mineralogical and REE-CM characterization and quantification. Advanced ML capabilities are key to integrate these multi-physics datasets for identifying hot zones and optimizing sensing technology deployment. Feature engineering ML jointed with federated learning and transfer learning will be used for data organization, feature extraction and privacy protection.

Project 7: Time Series Analysis - CLOSED

Project 8: Analyzing Dataset Popularity for Optimizing In-network Storage

Location: Onsite preferred

Required Skills: python, machine learning, data analysis

Abstract:

Scientific computing has seen a surge in large data transfers. Many of these transfers are redundant, with users repeatedly accessing the same data files for debugging or collaborating on related research topics. To mitigate this, regional data caches have been designed to reduce network traffic and latency, ultimately improving application performance. This project aims to investigate the popularity of datasets in regional data caches, with a focus on determining the predictability of data access patterns. We seek to inform the development of caching policies that can optimize network utilization. Our goal is to answer key questions such as: Which datasets are most frequently accessed? Can we identify patterns in data access behavior? And how can we leverage this knowledge to improve caching strategies?

Project 9: Predicting Laser Component Failures in Network Routers for Proactive Maintenance

Location: Onsite preferred

Required Skills: python, machine learning, data analysis

Abstract:

High-speed network routers and switches rely on lasers for data transmission, and unexpected failures can have significant impacts on network connectivity. We aim to develop a predictive maintenance tool that can identify potential laser component failures before they occur. Using digital monitoring data from ESnet core routers, this project will apply analysis algorithms for feature extraction and failure prediction. Our goal is to identify a reliable algorithm that can predict failure events with sufficient lead time, enabling proactive maintenance and minimizing network disruptions. Initial evaluation of the existing monitoring data has already revealed opportunities for improving the data collection process, and this project will continue to explore and refine the dataset. By developing a usable tool for the network operations center, we can validate the effectiveness of our approach and improve network reliability.

Project 10: Developing Packets - CLOSED

Project 11: Wireless Networking Field Measurement - CLOSED

Project 12: Leveraging AI/ML and data analytics to improve System Observability - CLOSED

Notes:

• Students must be enrolled in a full-time academic program at an accredited college or university. Proof of enrollment is required.
• Spring 2025 Term is 16 weeks (1/13/2025 - 5/2/2025). Summer is 12 weeks (6/2/2025 - 8/22/2025). Student participation requires 20 hours per week for Spring/Fall, and 40 hours per week for Summer appointments. A "late start" date can be considered for academic reasons.
• Work will be primarily performed in Berkeley, CA, or the Champaign, Bloomington, Illinois office, or remotely.
• The appointment can be renewed based on satisfactory job performance and operational needs.
• Salary will be predetermined based on student step rates.
• Positions 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.

Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov

Berkeley Lab is an Equal Opportunity and Affirmative Action Employer. In support of our rich 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.