The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years. Based on these predictions, a 127-fold increase of the IP traffic is expected from 2005 to 2021. It is also anticipated that the mobile data traffic will reach 3.3 zettabytes per year by 2021, and that the number of mobile-connected devices will reach 3.5 per capita.
With such demands for higher data rates and for better quality of service (QoS), fifth generation (5G) standardization initiatives, whose initial phase was specified in June 2018 under the umbrella of Long Term Evolution (LTE) Release 15, have been under vibrant investigation. In particular, the International Telecommunication Union (ITU) has identified three usage scenarios (service categories) for 5G wireless networks: (i) enhanced mobile broadband (eMBB), (ii) ultra-reliable and low latency communications (uRLLC), and (iii) massive machine type communications (mMTC). The vast variety of applications for beyond 5G wireless networks has motivated the necessity of novel and more flexible physical layer (PHY) technologies, which are capable of providing higher spectral and energy efficiencies, as well as reduced transceiver implementations.
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The Biomedical Imaging & Graphics (BIG) lab at the University of Saskatchewan, Canada, led by Dr. Ian Stavness, is hiring a fully-funded PhD Student or Post-doc in Computer Science / Electrical Engineering / Biomedical Engineering to work on deep learning in speech and neural signal processing. We plan combine powerful computer simulations of vocal tract biomechanics with neural and acoustic signals of speech production captured with state-of-the-art magnetoencephalography (MEG) measurement. The PhD/Post-doc will contribute to ArtiSynth (http://www.artisynth.org) an open source toolkit used by researchers around the world to create 3D models of tissue mechanics and fluid dynamics, as well as the development of deep learning architectures for brain/speech data.
We are searching for a bright and enthusiastic individual to join our team and make an expressive, real-time computer brain-speech interface a reality. The ideal candidate will have strong computer programming skills, experience with modern machine learning methods and a keen interest in biomechanics / biomedical / speech / brain research. Prior experience with deep learning, finite-element analysis, digital signal processing, biomechanics, software engineering, robotics, and/or controls is also desirable. The candidate will have opportunities to do an industrial internship with our partner on MEG sensing, as well as an academic internship with our collaborators at the University of British Columbia.
Interested applicants should send a letter indicating their interest and experience, a CV, and transcripts to firstname.lastname@example.org (please use subject line “Deep Learning Brain position”)
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