Multiple Fully Funded Ph.D. Positions at the Next Generation Networking Lab
We are looking for motivated students to work on several next-generation networking topics. The students will study, design, build, and deploy next-generation network infrastructure and protocols for various use cases such as Augmented Reality/Virtual Reality(AR/VR), Domain Science applications with Big Data (e.g., Climate Science, Genomics, Physics), Content Delivery Networks (CDNs), 5G mobile networks, and more. The successful students will join a team of highly innovative students and researchers in the area of next generation networking. They will have the opportunity to participate in multi-institutional and cross-domain collaborations. The positions are fully funded - they include full tuition and a competitive stipend. For more information on what we are working on, please visit our website at https://tntech-ngin.net.
- Number of positions: 2
- Deadline for applications: Open Until Filled
- Starting date: Fall 2021
- Salary and Benefits: Full Tuition Waiver and a competitive stipend.
- Contact: Susmit Shannigrahi firstname.lastname@example.org
Overview of the NGIN Lab
The Internet is the focal point of our daily lives. It influences all aspects of modern society - science, commerce, transportation, defense, and more. However, the unprecedented growth in traffic volume, users, and applications with different requirements continue to push the limits of today’s Internet. At the Next-generation Networking Lab (ngin), we investigate how we can align the network protocols to next-generation applications’ requirements.
Position 1: Measurement of Internet Errors
The student will work with a multi-institutional team to measure and understand the errors that happen in today’s Internet. Once we understand what type of errors are happening in the Internet, we will seek to find new error checking mechanisms for the Internet to safely move large amounts of data efficiently and without errors.
The three aspects of this project are:
- Developing and deploying error detecting software. We will deploy this software on operational networks to collect error samples.
- Analyzing and understanding error patterns from the collected datasets. For this phase, we will utilize traditional data analysis mechanisms and/or machine learning models.
- Developing new error detection and correction methods (e.g., Checksums, CRCs) that will be suitable for big data of the future.
This position is funded by a grant from the National Science Foundation.
Position 2: Next-generation networking for big-science data
The student will work with a multi-institutional team working on designing and deploying next-generation Internet protocols for huge volumes of data. The student will work in close collaboration with scientists from High-energy Particle Physics and Genomics community.
The student will:
- Design next-generation network protocols that facilitate efficient publication, discovery, and transport of large amounts of data (Petabytes to Exabytes)
- Integrate these protocols with existing tools and workflows used in the big science communities
- Performance measurement and optimization of workflows and network stack.
This position is funded by a grant from the National Science Foundation and an internal grant from Tennessee Tech.
The applicants should have the following qualifications:
- A Master’s degree in Computer Science or related fields. Exceptional undergraduate students will be considered.
- Strong background in algorithms and programming (At least one of C, C++, Python)
- Excellent analytical skills
- Good verbal and written communication skills
- Interest in exploring and learning new technologies
- The university has waived the GRE requirement for Fall2021. However, please include them if available.
- Experience in mathematical modeling
- Familiarity with network simulation tools (NS3, MatLab, or similar)
- Hands-on experience in software development
How to Apply
Send an email to Dr. Susmit Shannigrahi at email@example.com with
- Your latest CV
- Academic transcripts
- Your GitHub link (if GitHub is not available, please send a brief description of what you have worked on)
- Any publications (PDF)