[MS] Research Opportunity: ML-Driven Path-Loss Modeling for Wireless Systems

The following announcement is from [Michael Neuman – mikeneum@usc.edu]. Please contact them directly if you have any questions.

Research Opportunity: ML-Driven Path-Loss Modeling for Wireless Systems

Advisor: Prof. Andreas Molisch

Supervising Ph.D. Student: Michael Neuman

Lab: WiDeS Lab, University of Southern California

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The WiDeS Lab is seeking motivated M.S. or B.S. students to join an active research project at the intersection of machine learning, ray tracing, and RF propagation.

This project focuses on advancing deep-learning-based path-loss models by training neural networks on high-fidelity ray-tracing data, enabling fast and accurate wireless channel simulations. The work supports ongoing research in the design of next-generation wireless systems.

You will work closely with a senior Ph.D. student and contribute to cutting-edge research by:

• Developing PyTorch-based neural networks for fast path-loss prediction
• Running simulations comparing ML models with ray-tracing and measurement-based path-loss results
• Proposing, implementing, and evaluating novel ML algorithms for wireless communications
• Participating in a collaborative, results-driven research environment with regular interaction with both the supervising Ph.D. student and faculty advisor

Requirements

• Major in Electrical Engineering, Computer Engineering, or Computer Science
• Strong proficiency in Python and PyTorch
• Ability to write clean, modular, well-documented, and efficient code
• Strong debugging skills for complex codebases
• Coursework in machine learning and advanced programming is preferred (highly motivated students with strong coding skills are encouraged to apply)
• Strong communication skills for both technical detail and high-level presentation
• Ability to commit sufficient time each week to meet project milestones
• Graduation date: Fall 2026 or later (commitment beyond one semester is expected)

Benefits

• Hands-on experience with cutting-edge wireless communications research
• Practical application of ML theory to real-world RF system engineering
• Opportunities to contribute to peer-reviewed publications
• Experience valued in both industry and academic research careers
• Potential for a paid student-worker position or enrollment for Directed Research (DR) credit after the first semester

Preferred Qualifications (Not Required)

• Basic understanding of electromagnetic propagation and antennas
• Introductory knowledge of wireless communications or current enrollment in EE 535

How to Apply

Interested students should email a CV and a brief statement of interest to:

• Michael Neuman Ph.D. Student, WiDeS Lab, mikeneum@usc.edu

Application deadline: January 19, 2026 Applications will be reviewed on a rolling basis, with priority given to those submitted by the deadline.

Published on January 7th, 2026Last updated on January 7th, 2026