The 2024 SURF program is designed to provide a paid 11-week hands-on research experience in communications technology. CTL's SURF projects offer virtual and in-person opportunities in Boulder, Colorado, and Gaithersburg, Maryland. Interns are awarded a $7,810 stipend, with travel and housing allowances available for those who live outside of the NIST immediate area. Applications are due January 31, 2024.
With expertise honed over decades of research in antennas and wireless propagation, materials science and electronics testing, as well as communications network protocols and standards, CTL serves as an independent, unbiased arbiter of trusted measurements and standards to government and industry. We focus on developing precision instrumentation and creating test protocols, models, and simulation tools to enable a range of emerging wireless technologies. Click here for additional information on CTL.
To apply for virtual positions, use the Gaithersburg application and indicates that working virtually is an option you.
Building the Quantum Internet with Microwave-Optical Quantum Transducers
Mentor - Tasshi Dennis, 303-497-3507, tasshi.dennis [at] boulder.nist.gov (tasshi[dot]dennis[at]nist[dot]gov)
Networking superconducting quantum computers will allow them to scale and reach unprecedented capacity far beyond classical computers. We are creating remote microwave entanglement with optical two-mode squeezed states and microwave-optical transducers. This in-person opportunity involves the characterization of a mechanical membrane transducer operated at millikelvin temperatures to understand thresholds for network operation. We offer hands-on experience with quantum optics, microwave electronics, control systems, and cryogenics. [In-person opportunity]
Developing Acoustic Absorption Metrology in Liquids
Mentor - Bobby Lirette, 303-497-6864, robert.lirette [at] nist.gov (robert[dot]lirette[at]nist[dot]gov)
Acoustic absorption spectroscopy is one of the few tools that can probe intermolecular bonds through relaxations. Knowledge about these bonds and relaxations is vital to the chemical manufacturing and pharmaceutical industries. For many chemicals of interest, these relaxations occur in the ultrasonic frequency range. We are working to standardize methodologies for measuring acoustic absorption in various liquids using through-transmission and pulse-echo methods. The student will be working with their mentor to develop and improve methods for measuring ultrasonic absorption. They will gain experience in computational data analysis, instrument control, and hands-on experiment design. [In-person opportunity]
Building Toward the Next Generation of Rydberg Atom Sensors
Mentor(s) - Aly Artusio-Glimpse and Nik Prajapati, 303-497-5661, alexandra.artusio-glimpse [at] nist.gov (alexandra[dot]artusio-glimpse[at]nist[dot]gov)
Rydberg atom electric field sensors are a highly attractive alternative to classical receivers. These devices are self-calibrated, linking field strength to atomic and fundamental constants of nature, are naturally stable, highly sensitive, and widely tunable over an extraordinary range of frequencies (DC-THz). The Electromagnetic Fields Group has an in-person opportunity for a motivated candidate to support the continued advancement of Rydberg atom sensors. This project will focus on the development of small-package Rydberg atom sensors, and the student will utilize their electrical and/or optical engineering skills to do so. A student working on this project will learn basic atomic physics concepts that pertain to the function and operation of these sensors and will get hands-on experience with various laser systems and control infrastructure. Some prior hands-on experience with electronics or optics is preferred. [In-person opportunity]
Simulation and Modeling of Future Wireless Communication Systems Mentor - Wesley Garey, (301) 975-5190, wesley.garey [at] nist.gov (wesley[dot]garey[at]nist[dot]gov)
Every new generation of wireless communication technologies is more capable but more complex than the previous generation. Wireless systems require collaboration by hundreds of organizations worldwide and take years to design, test, and implement. An important part of the development process is computer simulations of proposed network technologies using tools like NS3 (https://www.nsnam.org/). NIST actively works with the NS3 developer community to create new modules that incorporate the latest wireless technologies. The SURF applicant selected to work on this project will help extend the simulation models of the psc-ns3 codebase (https://github.com/usnistgov/psc-ns3) that WND has developed and that researchers use to investigate existing and emerging wireless communication technologies. These include Public Safety Communication (PSC) applications, Fifth Generation (5G) New Radio (NR) Sidelink (SL) communication, Proximity Services (ProSe), and simulation visualization tools (https://github.com/usnistgov/NetSimulyzer). This task involves working with a team of developers to design, implement, document, and test new capabilities in the psc-ns3 codebase and will primarily involve using the C++ programming language and Linux to implement and run ns-3 simulations. [In-person or virtual opportunity]
Deployment and Evaluation of 5G Open RAN Testbed Mentor - Fernando Cintron, 301-975-6353, fernando.cintron [at] nist.gov (fernando[dot]cintron[at]nist[dot]gov)
The Wireless Networks Division (WND) works with industry, academia, and other government entities to develop, deploy, and promote emerging technologies and standards for wireless networks. One area of interest within WND is the evaluation of next-generation networks based on open-source solutions compliant with the Open Radio Access Network (O-RAN) Alliance specifications. The selected candidate will work on the performance assessment of Fifth Generation (5G) testbed deployments and the interaction of individual components within the network, such as the RAN Intelligent Controller (RIC). The selected candidate will help the team evaluate some of the components and interface capabilities and compliance with ORAN Alliance specifications. In addition, the selected candidate may work on the testbed towards specific features of 5G RAN, such as RAN slicing and resource orchestration. Desired skills and tools: Linux shell scripting, git, LaTeX, communication skills, writing skills, programming skills, networking tools experience (iperf3, Wireshark), and experience with Software Defined Radios (SDR) are preferred but not essential. [In-person opportunity]
Machine Learning-Assisted Radio Frequency Sensing Mentor - Jack Chuang and Jian Wang, 301-975-4171 and 301-975-8012, jack.chuang [at] nist.gov (jack[dot]chuang[at]nist[dot]gov) and jian.wang [at] nist.gov (jian[dot]wang[at]nist[dot]gov)
Artificial intelligence (AI) has been in the news a lot since the debut of ChatGPT a year ago, because of the power of large language models like it to perform functions previously restricted to humans, such as generating prose and writing programs. But AI is a powerful tool that is being used in other areas of science and engineering, including developing the next generation of wireless communications systems. In addition to supporting very high-speed and very low-latency data transmissions, tomorrow’s wireless networks will also integrate sensing capabilities that can detect the presence of nearby people and objects, such as cars, obstacles, and unmanned aerial vehicles (UAVs). The Wireless Networks Division in the Communications Technology Laboratory at NIST is investigating how to automate sensing so that an Integrated Sensing and Communications (ISAC) system can identify the major features of its environment and adapt to changing conditions. This will involve training AI systems to recognize the signatures of radio waves reflected from nearby objects and distinguish them from background noise. The student will use data collected from NIST’s sensing experiments involving radio waves at frequencies in the 10s of gigahertz that will be used by future wireless networks to train AI systems to identify and locate clusters of reflected radio waves and then validate the performance of the AI system that they trained. Desired Skills: Applied machine learning, MATLAB and/or Python programming languages, Git version control, and strong communication abilities. [In-person opportunity]
Deployment and Evaluation of Secure Virtualization Environments for 5G O-RANs Mentor(s) - Doug Montgomery and Scott Rose, 301-960-3630 and 301-975-8439, dougm [at] nist.gov (dougm[at]nist[dot]gov) and scott.rose [at] nist.gov (scott[dot]rose[at]nist[dot]gov)
5G Open-Radio Access Networks (O-RAN) technologies seek to transform radio access networks from single vendor solutions based upon proprietary appliances to a disaggregated network architecture of components and functions, with standardized open interfaces, and designed to be deployed in virtualized and cloud native environments.
NIST has recently actively engaged in O-RAN Alliance standards development, focusing on enhancing the security of virtualized, cloud native O-RAN functions. This area has both the greatest potential to increase overall network security and the greatest potential risk to the eventual commercial viability of O-RAN technologies. This project will involve enhancing existing 5G O-RAN laboratory testbeds to support emerging security standards, recommendations, and technologies for cloud native virtualization environments (e.g., Kubernetes, OpenShift, etc.), evaluating the ability of existing open-source virtualization environments to support such security requirements, and employing existing open-source tools to actively test the security of virtualized network environments to evaluate and verify secure configurations. Desired skills / experience: Linux, Kubernetes / Docker, service-based architectures, Python, dev-ops / network programming, network protocols / tools / technologies (http, TLS, PKI, OAUTH, Wireshark), penetration testing concepts and tools. [In-person opportunity]
Development of a Web Based Test Systems for Internet Security Routing Technologies Mentor(s) - Doug Montgomery and Oliver Borchert, 301-960-3630 and 301-975-4856, dougm [at] nist.gov (dougm[at]nist[dot]gov) and oliver.borchert [at] nist.gov (oliver[dot]borchert[at]nist[dot]gov)
NIST is actively involved in the design of standardization of technologies to detect and mitigate “route leaks” in the Internet’s global routing infrastructure. Such route leaks lead to catastrophic outages in large subsets of the Internet that can last for several hours or days. NIST has been instrumental in standardizing a route leak mitigation technique called Autonomous System Provider Authorization (ASPA) and has developed stand-alone test tools and reference data sets to test implementations of ASPA in commercial routers. This project will take these initial stand-alone ASPA test tools and implement a web-based front end to evolve the tools into a distributed testing service that enables remote testing and automated results evaluation of remote ASPA implementations. Desired skills: web front-end development (HTML, CSS, CSS frameworks), Python, Flask, network programming, SQL, Git, Linux, working knowledge of Internet protocols (DNS, BGP). [In-person opportunity]
easyEXPRESS: NIST’s First Visual Studio Code Extension Mentor - Allison Barnard Feeney, 301-975-3181, allison.barnardfeeney [at] nist.gov (allison[dot]barnardfeeney[at]nist[dot]gov)
Industrial Artificial Management and Metrology: Process Simulation Testing and Development Mentor - Michael Sharp, 301-975-0476, michael.sharp [at] nist.gov (michael[dot]sharp[at]nist[dot]gov)
This work will present a student with an opportunity to assist in the testing and development of novel software tools designed to evaluate risks and opportunities for AI tools in an industrial production process. This project will focus on evaluating a multistage production process through both simulated and physical sensing methods. The effort will center on tools for assisting in the efforts of digital twin technologies and systems monitoring. The student will work closely under the direction and supervision of software development experts and NIST AI experts to create and test a process simulation for a major government manufacturing center. Experiments and tasks may include, but are not limited to: (a) implementing an AI-driven COTS monitoring system; (b) developing best practice methods for the design and creation of high-level digital simulators; (c) determining mechanisms and methods for rapidly executing process redesigns and ‘what if’ scenarios; and (d) creating measures and metrics for capturing the risks and returns of AI-driven technologies on an industrial manufacturing process. The goal of this project is to help lower barriers and provide intuitive recommendations for US manufacturers looking to enhance their productivity through AI monitoring, controls, and design technologies. Required skills: Python coding and/or significant coding experience; beyond high school level classes in engineering, computer science, or statistics; ability to work with a team. Recommended: good communication skills; writing experience; knowledge of technical risk and reliability for production processes; understanding of basic AI methodologies; AI software packages. [In-person or virtual opportunity]
P4 Programming for 5G QoS - Mentor - Lotfi Benmohamed, (301) 975-3650, lotfi.benmohamed [at] nist.gov (lotfi[dot]benmohamed[at]nist[dot]gov)
This project seeks to develop an SDN based solution to classify and prioritize 5G network traffic, using the P4 programming language. We are deploying an experimental 5G network, using both open-source and proprietary components. A major benefit of 5G network is 5G slicing, which allows different types of traffic (e.g., general Internet browsing, remote driving, industrial sensing) to share the same physical network with minimal interference with each other. A requirement for 5G slicing is the enforcement of Quality-of-Service (QoS), so that high priority traffic does not suffer slowdowns due to higher volume of low priority traffic. Unfortunately, the current software 5G implementations do not natively support dataplane QoS. The goal of this project is to explore a potential solution based on using SDN switches to enforce 5G QoS. In particular, a P4 program installed on a physical SDN switch would recognize 5G packet headers, classify each packet into different traffic classes, and enforce QoS through egress queuing and advanced scheduling methods. The applicant must have prior experience in P4 programming including how to write a protocol parser and how to perform QoS enforcements, a deep understanding of common network protocols such as IP and UDP, as well as prior knowledge in 5G network and its protocols. Note that packet traces for the relevant protocols will be provided, and the applicant is expected to learn about the protocol structure by reading these packet traces using tools such as Wireshark. [In-person or virtual opportunity]
Internet of Things (IoT) Device Interoperability Mentor - Eugene Song, 301-975-6542, ysong [at] nist.gov (ysong[at]nist[dot]gov)
Internet of Things (IoT) devices serve key functions in intelligent transportation systems, such as providing sensed data or data received from external networks to perception systems using sensors and embedded devices (e.g., on-board units, road-side units). However, this data is received from a large diversity of sources, and one major challenge is ensuring interoperability between the different IoT devices and systems that generate, transmit, and use the data. Interoperability is the ability of two or more systems to exchange information and to use the information exchanged based on the standardized communication protocol to achieve the functions and goals. Several interoperability challenges for intelligent transportation systems include diverse cellular vehicle-to-everything (C-V2X) connectivity (e.g., 3GPP LTE/4G/5G), diverse proprietary and standardized protocols adopted by different vendors and manufacturers, and diverse use cases and contexts. Interoperability testing, measurement, and assessment methodologies are keys to overcoming these challenges to assure the interoperability of IoT devices. This project will focus on developing an interoperability analysis methodology for IoT devices in automated vehicles. The student will learn about the concept of device and system interoperability, research interoperability testing methods, and develop an open-source software tool to analyze the interoperability of IoT devices based on standard communication protocols. The tool will be validated against use cases and packet data provided by NIST researchers working collaboratively on the project. Desired skills: Java or Python programming experience required; computer science or computer engineering major preferred; experience using Extensible Markup Language (XML) preferred. [In-person opportunity]
Developing Simulation Tools for Automated Vehicles Mentor - Thomas Roth, 301-975-3014, tpr1 [at] nist.gov (tpr1[at]nist[dot]gov)
An automated vehicle is expected to be competent in planning, monitoring, and performing behaviors that today are executed by human drivers. These behaviors include maintaining a lane under various adverse operating conditions, navigating an intersection including pedestrian traffic, and others. The automated execution of these behaviors must be both safe and predictable to meet the expectations of human drivers and other humans in the driving environment. The use of network communications between vehicles and between a vehicle and other devices (including road-side infrastructure and smaller devices such as cell phones) is expected to improve the safety and reliability of automated vehicles. One approach to validate these claims is simulation, with the network dynamics modeled in simulators such as ns-3 and the traffic dynamics modeled in simulators such as CARLA, SUMO, or MATLAB. Simulations can be used to test vehicle behaviors in a virtual environment prior to field deployment with low cost and low risk to humans.
This project is focused on the development of a simulation environment that combines ns-3 (network simulation) with CARLA (vehicle simulation) for vehicle-to-vehicle communication scenarios. The student will learn about the challenges preventing the widespread adoption of automated vehicles, receive training in the NIST simulation tools for automated vehicles, design simulations of driving scenarios in CARLA to stress test these tools, and develop C++ extensions to add support for new types of driving scenarios. This work will be performed collaboratively with a team of NIST researchers. Desired skills: experience with object-oriented programming required; experience with C++ programming language preferred; computer science major preferred. [In-person preferred]
Simulation of Emergency Traffic Management for Smart Cities Mentor - Wendy Guo, 301-975-5855, wguo [at] nist.gov (wguo[at]nist[dot]gov)
The generation and effective communication of evacuation plans are critical requirements during civil emergencies and disasters. In emergency situations, managing traffic is of utmost importance to the safety of both road users and first responders, facilitating unrestricted egress for evacuations and road access for emergency personnel. The traffic management system plays a vital role in establishing and maintaining control points, restricting area access, and facilitating controlled transit through the incident site. Simulation of the potential response of road traffic to different disaster situations can help ensure that evacuation plans are robust and effective for a given community. These simulations can be conducted using SUMO, a software tool to model and analyze traffic, together with network simulators such as OMNeT++ that model the different cyber and social networks used to coordinate disaster response. These simulators can be synchronized to exchange data at runtime, enabling online simulations that model the impact of communication networks on road traffic (and the reverse). A software package called Veins provides this integration between SUMO and OMNeT++ with a comprehensive suite of realistic network models.
This project focuses on developing a traffic network simulation for emergency situations. The objective is to guide the student in setting up a simulation platform comprising SUMO, OMNet++, and Veins. The student will learn how to design simulations with real map settings under various scenarios, collect data from the simulations, and analyze it to derive meaningful insights for guiding emergency evacuations. The work will be a collaborative effort with a team of NIST researchers. Desired skills: experience with object-oriented programming is required; computer science major or experience with network design/simulation is preferred; interested in smart city topics and traffic network management. [In-person preferred]