ML models including data-driven, unsupervised models that can extract features from extensive datasets (such as a library of unlabeled RF signals). ML models including data-driven, unsupervised models that can extract features from extensive datasets (such as a library of unlabeled RF signals).

•Analyze and choose ML strategies and algorithms to solve signal processing and RF problems for software-defined radio and signal analysis solutions, both at the edge and in the cloud • Define validation strategies for chosen ML algorithms and solutions.•Proficiency with a deep learning framework such as TensorFlow or Keras Python and basic libraries for machine learning such as scikit-learn and pandas statistics, statistical analysis and algorithms, C++

Eligible for TS/ SCI /Poly clearance.