The project, which is partly funded by the National Science Foundation, aims to explore the various ways that Machine Learning can provide a deeper understanding of multipath propagation and enable extrapolation and prediction of the channels. Of particular interest is developing new machine learning approaches that reduce the amount of required training data/measurements which tends to be costly in wireless systems.
The University of Southern California is ranked among the top-10 universities in the US in engineering (US News and World Report). Our team combines expertise in the fundamentals of wireless propagation channels (Prof. Molisch) and Machine Learning (Prof. Soltanolkotabi); for details see https://wides.usc.edu and https://viterbi-web.usc.edu/~soltanol/ . The PostDoctoral Fellow will have the opportunity to interact with these groups as well as other groups across campus. Mentoring and career support will be provided to the successful candidate.
The successful candidate should have a solid background in Machine learning and its fundamentals. Familiarity with computational tools like TensorFlow, wireless propagation channels, wireless localization, and wireless system design is a major advantage. A good publication record is expected. A CV with details of the applicable experience, skills, and publications, and contact details (or recommendation letter) of at least one reference should be sent to molisch at usc.edu and soltanol at usc.edu .
Applications from female researchers and underrepresented minorities are particularly encouraged.