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

About the role

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.

Responsibilities

• 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.

Requirements

• Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, or related field.
• Strong background in digital communications / 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.

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.