Summer Internship

2024 WINLAB Summer Internship

The WINLAB Summer Internship Program offers full and part-time summer internships in a university research setting to highly talented undergraduate and graduate students.  The main goal of the program is to provide students with a real-world, team-based research experience in various topics related to wireless technologies.  Each intern joins an active research group consisting of a mix of graduate and undergraduate students with at least one mentor who is a faculty member.  All projects are designed to be completed within the duration of the program, but can also be extended for eligible students to the following academic year.  Each week students are expected to report on the progress of their work in a summer research group meeting.  At the conclusion of the program, interns submit a report and are required to give a final presentation on the research results.  A limited number of full-time internship students receive a monthly stipend plus an on-campus room in designated Rutgers dormitories (available for non-Rutgers, full-time interns ONLY!). Additional students will be offered part-time (hourly) summer employment. The opportunity for non-paid participation may also exist once the paid positions are distributed. Should you be interested in one of those positions if not chosen for a paid position, please make sure to indicate that on your application. The program for 2024 will be fully in person and will begin on Tuesday, May 28th and end on Wednesday, August 7th. Please note that the WINLAB facility is NOT on any of the Rutgers bus routes and that getting to/from the lab requires personal transportation. 

To apply for the 2024 WINLAB Summer Internship Program, students must be currently enrolled full time in a college or university, be eligible to work in the US, have an anticipated graduation date of 2025 or later, and complete the following five steps:

  1. Obtain a copy of your transcript.  If you are a Rutgers student, an unofficial copy is sufficient.
  2. Please obtain two letters of reference.  Letters of reference should be submitted to internship (AT) winlab (DOT) rutgers (DOT) edu by faculty at your home institution or past job supervisors who can assess the quality of your academic performance and research potential. If you are a Rutgers student and will be using a WINLAB professor(s) as your reference(s), you do not need a letter of reference from them.  Simply list the name of the professor(s) in the reference section of the application form since the WINLAB faculty will be asked for input regarding any student who lists them as a reference.
  3. Write a brief essay (no more than one page) on why you would like to join the program, what strengths you will bring to the program and what you hope to achieve by being included in the program.  Please see the list of research topics at the bottom of this page and advise which projects peak your interest.  Please understand that we cannot promise that you will be assigned to the project you ask for, however, we will make an effort to put accepted students in their areas of interest.
  4. Prepare a CV/resume.
  5. Complete the application form  with the above transcript, essay and CV uploaded no later than April 14th. Due to the number of applications that we anticipate receiving this year, incomplete applications or those that are received after the deadline may not be considered. The selection of interns will be determined by the WINLAB faculty members.  All accepted undergraduate and graduate students will be notified by email of their acceptance into the program by April 28th.

2024 Summer Internship Dates

Applications Due: April 14
Notifications: April 28
Internship Starts: May 28
Internship Ends: Aug 7

Project Pages

Past Research Topics

2024 Research Projects

Project Pre-Requisites
Multistatic RFID interrogation & localization with Cosmos/Orbit: Cosmos/Orbit software-defined radios will interrogate and receive the backscattered information from commercial ultra-low cost batteryless RFID tags. Distributed interrogation boosts SNR and robust-to-multipath WINLAB localization techniques will be examined. OS: Linux
Software: C/C++
Cloud-native O-RAN: 5G/6G wireless networks based on O-RAN architecture will have O-RAN Network Functions deployed on the O-cloud infrastructure. Open source projects such as O-RAN SC, ONAP, Nephio, OAI are actively engaged in developing open-source software which can be used in O-RAN networks. In this project, the students will implement an O-cloud infrastructure hosting one or more cloud-native O-RAN Network Function(s), and develop software to support monitoring and management, using energy consumption as an example OS: Linux
Software: C/C++, Python
Open-source/cloud experience preferred
AR/VR visualization of data: This project develops AR/VR solutions for visualizing both small and large volumes of data, enabling immersive and interactive exploration of complex datasets. It focuses on optimizing data representation in virtual environments to support effective analysis and decision-making across diverse application areas. OS: Windows, Linux
Software: Unity, C#, C/C++, Python, PyTorch, TensorFlow
Machine Learning for Enabling 5G and Satellite Network Coexistence in FR3 Spectrum: GPT
This project applies machine learning algorithms to facilitate the coexistence of 5G and satellite networks in the FR3 spectrum, analyzing data to optimize spectrum sharing and interference management between terrestrial and space-based communication systems.
OS: Linux
Software: Python, C/C++, TensorFlow, PyTorch
Magic Room: Device-free sensing of People using batteryless tags and machine learning: Batteryless, ultra-low cost RFID tags can serve as additional ultra-low cost antennas; variations of the multipath propagation inside a room offers impressive information for estimating number of people and their tracks inside a room; WINLAB techniques, including machine learning, will be tested. OS: Linux
Software: C/C++, Python
Channel Measurement Campaign for Data Analytics in Wireless Networks: This project will develop an SDR based platform for channel characterization and coverage measurements on COSMOS/ORBIT testbeds and perform a series of measurement campaigns. Once the platform is developed, a series of measurement campaigns will need to be performed at various locations to validate the platform’s performance and accuracy. These campaigns will involve deploying the platform at different locations on the COSMOS/ORBIT testbeds, capturing wireless signals from different sources, and analyzing the data to derive insights into the wireless channel characteristics and coverage.
OS: Linux
Software: C/C++, Python, GnuRadio, PyTorch, TensorFlow
Self-Driving Vehicular Project: This project will assemble and train miniature autonomous vehicles to run in the miniature smart city environment, by using low latency networks for vehicular control. This project will use specialized low latency cameras and radios to operate remote model cars and design and implement self-driving algorithms using machine learning libraries in python. Students will design behavior that will allow the vehicles to react realistically to other cars and props in the smart city environment, and work with the testbed infrastructure to use external data from the intersection to improve performance. Another goal is to offload localization, mapping and navigation to MEC using 5G and get back commands to control the wehicle/robot. OS: Linux, RoS
Software: C/C++, Pytorch, Tensorflow
COSMOS Emulation of 5G and Satellite Network Coexistence in FR3 Spectrum: This project focuses on the emulation of 5G and satellite network coexistence within the FR3 spectrum using Software-Defined Radios (SDRs) on the COSMOS platform. It aims to evaluate spectrum sharing mechanisms and interference mitigation techniques between terrestrial and space-based networks. OS: Linux
Software: Python, C/C++, GnuRadio
Digital twin for network energy consumption: The cost of energy consumption of wireless networks is steadily increasing as more equipment is deployed to meet rising user demand. To reduce energy costs, operators are using energy-savings solutions which modify the network configuration to reduce energy consumption when the user/traffic requirements are lower. These adjustments are made during time windows and in spatial regions where there is an opportunity for energy savings. In order to develop and test optimization applications and train AI models, there is a need for generating sample data for different network topology and scenarios. In this project, students will research models which estimate energy consumption of network nodes based on load scenarios, and then implement a digital twin of a network topology which can generate sample data for network load and energy consumption OS: Linux
Software: Python, C/C++
Data science skills preferred
Plant Doctor: batteryless basckatter radio sensors for plants and agriculture: Ultra-low cost sensors based on published results from WINLAB members will be re-built, exploiting (ambient) backscatter radio and batteryless principles. The goal is to offer 24hours/7days of week, continuous, wireless, batteryless monitoring and also experiment with new sensor (both analog and digital) designs and modalities. OS: Linux
Software: C/C++
AR Mural: This project involves developing an augmented reality (AR) based art platform that allows users to contribute their artwork, including paintings, photos, videos, and sculptures, to a set of locations. The 3D sculpture building feature will enable users to create and contribute to 3D sculptures that will be shown simultaneously across multiple locations. The team will need to develop the necessary tools and algorithms to enable collaborative sculpting, as well as implement the required backend infrastructure and communication protocols to ensure that the art can be displayed in real-time across multiple locations.
OS: Windows, Linux
Software: Unity, C#, C/C++

Convergence of mmWave and UHF batteryless ultra-low cost RFID technology: The basic limitation of RFID technology is the sensitivity of the RF harvester and the availability of energy close to the tag; this problem will be tackled with mmWave transmitters and careful redesign of the tag antenna, so that mmWave can be utilized for enegy harvesting and UHF for backscatter communication.

OS: Linux
Software: Python, C/C++
 Developing an LLM-Based System Architecture for Semantic Understanding in Smart Spaces: This project is focusing on developing an innovative system architecture for smart spaces that harnesses Large Language Models (LLMs) to understand and respond to multimodal inputs. It offers the chance to work on integrating LLMs into smart environments, creating libraries for seamless system interaction, and developing novel applications, all while gaining hands-on experience in AI, NLP, and software engineering within a pioneering research context. Use multimodality (including visual and lidar sources) and 5G networks to create situational awareness in various indoor and outdoor environments. OS: Linux
Software: Python, C/C++, Pytorch, Tensorflow
Real-time, robust, and reliable (R^3) machine learning over wireless networks: Edge computing is an emerging paradigm for enabling ML/AI applications in mobile networks. This setting presents many challenges in terms of latency, accuracy, security/privacy, and adaptivity to changing environments. The goal of this project will be to implement and evaluate algorithms and approaches which work “on paper”  to see how well they work in practice. In particular, students will work on methods for mobile devices to strategically “offload” complex computing tasks to the cloud, approaches for fully decentralized model updating and adaptation that are robust against malicious attacks, and strategies that can enable real-time tracking and control. OS: Linux
Software: Python, PyTorch, TensorFlow
Data Center Infrastructure Management: The program offers a hands-on training in Linux operating system administration and tools for efficient deployment and management of a modern data center infrastructure. The topics of study include virtualization, system commands, fundamentals of networking, file systems, python, server configuration automation with ansible.  One of the projects will be setting up a warewulf operating system provisioning platform for stateless Linux installation. OS: Linux
Software: Python
Remotely Piloted Vehicles: Low latency networking is an emerging technology for use in remote sensing and control of vehicles and robots. In this project, interns will develop software for remote piloting vehicles with both mecanum and swerve steering using low latency networks.  In addition, additional sensing, such as additional cameras, range sensors, and audio feedback will be added to aid the pilots. At the end of the project interns will demonstrate remote piloting a vehicle attached from anywhere in the Internet with speed, precision and accuracy, in a modeled urban environment. Interns will also evaluate the strengths and weaknesses of remote piloting interfaces for ground-based vehicles. OS: Linux
Languages: Python, C
AI for behavioral discovery: The past 40 years has seen enormous increases in man-made Radio Frequency (RF) radiation. However, the possible small and long term impacts of RF radiation are not well understood. This project seeks to discover if RF exposure impacts animal behaviors. In this experimental paradigm, animals are subject to RF exposure while their behaviors are video recorded. Deep Neural Networks (DNNs) are then tasked to correctly classify if the video contains exposure to RF or not. This uses DNNs as powerful pattern discovery tools, in contrast to their traditional uses of classification and generation. Interns will evaluate the accuracies of a number of DNN architectures on pre-recorded videos, as well as describe any behavioral patterns found by the DNNs. OS: Linux
Software: Pytorch, Shell,
Privacy Leakage Study and Protection for Virtual Reality Devices: Augmented Reality/Virtual Reality (AR/VR) technologies have been rapidly gaining popularity over the last decade. Various sensors, which enable immersive human-computer interactions, are embedded in the headsets and controllers to track the user’s position, body movements, surroundings, and inputs. The data embedded in these sensors encodes various types of the user’s private information (e.g., text input, voice commands, conversations, etc.), which can cause severe privacy breach if they are exposed to an adversary. This project aims to study sensor data management in commercial VR headsets (e.g., Meta Quest, HTC Vive Pro). Students working on this project will learn how to collect sensor data from the VR headset & controllers and analyze the potential of private information leakage. They will also develop privacy protection techniques to counteract the privacy leakage. OS: Windows, Linux
Software: Unity, C#, C/C++