Postdoctoral Scholar Wireless Communications, AI (RL/LLMs), and Software-Defined Radios

Position overview

Salary range: A reasonable estimate for this position is $69,073-$82,836, based on experience level at time of hire. See Salary Scale

Application Window

Open date: April 30, 2026

Next review date: Saturday, May 16, 2026 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee.

Final date: Thursday, Apr 29, 2027 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.

Position description

We are hiring a Postdoctoral Scholar to join an interdisciplinary research team developing an AI-enabled framework that translates high-level mission objectives into deployable communications signal-processing (DSP) solutions. The role combines (i) strong foundations in digital communications and statistical signal processing with (ii) hands-on implementation using reinforcement learning (including multi-agent RL), large language models (LLMs), and software-defined radio (SDR) testbeds.

You will help build and validate an architecture where specialized communications 'agents' collaboratively co-design end-to-end transmit/receive processing chains under formal performance and hardware constraints (e.g., BER, throughput, bandwidth/SEM, latency, and platform budgets).

Responsibilities

1. Communications DSP research & prototyping: develop/benchmark modulation, coding, synchronization, spectral shaping, equalization, and related receiver/transceiver components in simulation and on SDR platforms.
2. AI for communications implementation: implement and experiment with multi-agent reinforcement learning workflows that coordinate component choices and parameters while enforcing system-level feasibility.
3. LLM-assisted engineering workflows: contribute to domain-adapted LLM tools for proposing candidate DSP structures and parameterizations
4. Verification & test automation: validate designs using established tools (e.g., MATLAB, GNU Radio) and develop tests for communications metrics
5. Hardware-aware evaluation (desired): participate in SDR/hardware-in-the-loop experiments and help bridge algorithm design with realistic RF/platform effects.

Required qualifications

1. Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, or related field.
2. Strong background in digital communications / signal processing (e.g., modulation/coding/synchronization; performance evaluation with BER/throughput/bandwidth constraints).
3. Demonstrated experience implementing research software (Python; and/or C/C++) and running simulation studies.
4. Familiarity with modern machine learning tools and techniques

Preferred qualifications

• Experience with reinforcement learning, especially multi-agent or distributed/consensus-style coordination.
• Experience with LLMs for technical tasks (prompting, evaluation, or fine-tuning).
• Hands-on software-defined radio experience (e.g., GNU Radio, MATLAB toolboxes, USRP-class radios) and/or hardware-in-the-loop validation.

A reasonable estimate for this position is $69,073-$82,836 , based on experience level at time of hire.

Applicants are expected to have a doctoral degree, at the time of hire, in Electrical Engineering, Computer Science or a related field from an accredited university. Qualified candidates should send their curriculum vitae along with the names and address of three references to the following on-line recruitment URL to submit the materials requested.

Qualifications

Basic qualifications (required at time of application)

1. Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, or related field.
2. Strong background in digital communications / signal processing (e.g., modulation/coding/synchronization; performance evaluation with BER/throughput/bandwidth constraints).
3. Demonstrated experience implementing research software (Python; and/or C/C++) and running simulation studies.
4. Familiarity with modern machine learning tools and techniques

Application Requirements

Document requirements

• Curriculum Vitae - Your most recently updated C.V.
• Cover Letter (Optional)
• Statement of Research (Optional)
• Reflective Teaching and Mentoring Statement - See our guidance for writing a reflective teaching statement. (Optional)

Reference requirements

• 3-5 required (contact information only)

Job location
Irvine, CA