Work for Eric: Job Listings

The technical folks on the project will develop a mobile telephone application and network emergency services router to do a proof of concept of priority communications in a WiFi network, with the possibility of extending the project to 5G. 

The policy folks on the project will be monitoring, summarizing, and contributing to FCC, NTIA, and other rule makings and notices of inquiry. 

Both lines of inquiry feed each other - this is a single team, not two separate projects.

Skills sought

All candidates must have excellent research, writing, and analytical skills. 

There are two distinct areas for GRAs. CS and ECE students with knowledge of network protocols; wireless communications; real-time protocols such as SIP, RTP, HLS; mobile application development (Android or Apple); and network security are welcome to apply. SPIA students interested in communications policy are welcome to apply. 

Strong candidates will have experience and familiarity with wireless communications. 

We expect to hire more than one GRA for this project, so apply if you match at least half the requirements. Computer Science / ECE students are encouraged to apply.

Programming in C++ is highly preferred. If you do not know C++ and can’t learn it in two weeks, you’re unlikely to be considered. If you know C++ and have a good reason to use another language, we will consider it.

SPIA students familiar with navigating government filing systems, such as those at the FCC’s Electronic Comment Filing System (ECFS) or  the NTIA’s public comment notes, would be an asset. However, if you don’t know those systems, you can learn them.  

Candidates must be fluent in English as they will be reading a lot, writing more, and likely to go on visits to the Hill, industry sponsors, and regulatory agencies to describe what we are doing, explain our results, and make policy recommendations.

The FCC has implemented many regulations requiring the deployment of cryptographically secure caller identification signaling in the all-IP network. 

This enables the carrier to put a notation on the call saying the number was verified. 

This notation is the “Caller Verified” or green check mark on mobile phones and “[v]” in some networks for landline Caller-ID. 

However, subscribers still might not answer a verified call from a number they do not recognize. 

The issue is the subscriber might want to answer the call. 

It could be a store trying to schedule a delivery, a school calling about a school closure, or a bank calling about a suspicious charge. 

One proposal to address this is to have the carrier display the caller’s logo, or brand, to the subscriber when the call arrives. 

The presumption is that while the subscriber might not recognize the caller’s number, they will recognize their logo.

This project will look at the economic impact of branded calling on answer rates, and how the answer rates impact the caller’s business. 

We hypothesize that branded calling will have different impact for different industry verticals. 

For example, while branded calling is likely to increase call answer rates for retail delivery scheduling and health care calls for scheduling, wellness checks, and tracing, we expect it to have little or perhaps even negative impact on debt collectors.

Examples are more sales, fewer missed deliveries, higher customer / constituent satisfaction, etc. 

This modeling will be for a select number of industry verticals. We expect GRA’s to model scenarios, run scenarios, and write up results.

We seek several individuals to research the literature on the status quo of liquify pools, existing market makers across decentralized exchanges, and the characteristics of arbitrage across tokens or coins with different exchange algorithms. 

GRAs will collect and analyze structural characteristics of various arbitrage schemes between different implementations.

GRAs will take notes, synthesize results, and ultimately make proposals for adoption by the Next G Alliance. 

We urgently need students interested in societal and economic needs and novel applications for 6G wireless systems. 

Students interested in wireless technology, power efficient communications, and using the communications network for energy optimization are also encouraged to apply.

Tasks include developing and enhancing techniques for obfuscating video, audio, large-file, and low bit-rate data streams. 

We expect success as we will be building on techniques proven to outwit AI/ML detection techniques developed by our prior DARPA RACE work. 

This position will require knowledge of Generative Artificial Intelligence analyzers and generators. 

You’ll help develop an architecture and implementation for popular IoT and mobile devices to provide difficult-to-detect data streams for stenographic obfuscation.

Tasks will include: the development and coding of radio propagation models; literature reviews; report writing, copy-editing, and other related tasks. 

Skills sought

There are two distinct areas for GRAs. CS and ECE students with knowledge of simulation, data interfacing, or wireless communications are welcome to apply, as are School of Public and International Affairs students interested in communications policy. 

 An understanding of quantitative data analysis will be helpful. 

Programming in MATLAB is required for the implementation positions. 

Strong candidates will have:

• Experience and familiarity with wireless communications.
• A background or demonstrated interest in spectrum modeling (physics background will suffice).
• An understanding of or willingness to learn Monte Carlo simulation techniques.
• An understanding or willingness to learn Web API techniques.

A candidate who has everything except MATLAB might be considered, but will need to build a strong case and demonstrate the ability to learn new environments quickly.

Policy

In addition to the expectations noted in the job description, GRAs must be able to: interview stakeholders from government, academia, and industry; take clear, complete notes during interviews; and conduct qualitative data analysis.