Postdoctoral Scholar — Wireless Communications, AI; RL​/LLMs), and Software-Defined Radios

Position Overview

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 strong foundations in digital communications and statistical signal processing with 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 such as BER, throughput, bandwidth/SEM, latency, and platform budgets.

Estimated salary range: $69,073 – $82,836 based on experience level at the time of hire. See Salary Scale.

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

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

• 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.

Irvine, CA

The University of California, Irvine is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories covered by the UC anti‑discrimination policy.