Signal Processing and AI Researcher

Oak Ridge National Laboratory

20d•Onsite

About The Position

We are seeking an R&D Associate who will focus on developing and applying advanced signal processing, wireless communications, and AI techniques to RF sensing, drone detection, and power grid waveform analytics. You will apply methods traditionally used in wireless communications and signal intelligence — including time-frequency analysis, interference modeling, multi-sensor fusion, and propagation modeling — to develop AI-enabled detection, classification, and triangulation algorithms for critical energy infrastructure applications. This position resides within the Grid Communications and Security (GCS) Group in the Energy Sensing, Analytics, and Communications (ESAC) Section, Electrification and Energy Infrastructure Division (EEID) at Oak Ridge National Laboratory.

Requirements

Ph.D. in electrical engineering, wireless communications, signal processing, or closely related discipline
1–4 years relevant post-Ph.D. experience
Demonstrated expertise in digital signal processing (time, frequency, and delay-Doppler domains)
Experience with RF signal analysis and wireless communication systems
Experience with SDR platforms (e.g., USRP) and IQ data processing
Experience with MATLAB and Python
Excellent written and oral communication skills.
Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory.
Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs.

Nice To Haves

Experience applying wireless signal processing techniques to non-traditional domains (e.g., infrastructure sensing, anomaly detection)
Experience with AI/ML for time-series and spectral data
Experience with RF propagation modeling or spectrum coexistence
Experience with interference modeling and mitigation
Knowledge of power grid disturbance signals or high-frequency waveform analytics

Responsibilities

Develop and implement advanced time-frequency and spectral analysis techniques for high-frequency voltage/current waveform data
Apply wireless PHY-layer concepts (synchronization, channel estimation, interference mitigation, multi-user detection) to grid disturbance detection
Develop RF-based drone detection and classification pipelines using IQ data and SDR platforms
Implement localization and triangulation algorithms using multi-sensor fusion (e.g., TDOA, AOA, RSSI-based methods)
Develop RF propagation and digital twin models for infrastructure environments (urban, substation, corridor scenarios)
Integrate physics-informed machine learning models with signal processing feature extraction
Develop prototype software tools for automated waveform analytics and real-time disturbance detection
Prepare proposals, reports, and peer-reviewed publications
Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success.