Post-Doctoral Research Visit F/M Unsupervised Machine Learning for Wireless Communications
Il y a 6 mois
Contexte et atouts du poste
Wireless communication systems involve collecting large amounts of data related to electromagnetic propagation, which is normally used for the purpose of data transmission and demodulation, and then immediately discarded. While it is clear that leveraging the statistical aspects of propagation information (e.g. through learning the characteristics of its distribution and applying appropriate statistical techniques) has the potential to greatly enhance the performance and range of services offered by the network, this approach faces the practical challenges of real-time processing such as a limited computing and storage resources.
Mission confiée
The successful candidate will contribute to the development of advanced machine learning approaches for the processing of real-time signals in wireless communications systems, and in particular of the technique of channel charting [FGST23]. She or he will devise innovative theoretical approaches for distributed, real-time dimensionality reduction applied to multi-sensor systems; prototype these approaches trough simulations and/or lab experiments; write technical reports and articles for major conferences and journals in the field of wireless communications and machine learning; and regularly present the obtained results in scientific conferences and events.
The proposed topic lies at the crossroads of unsupervised machine learning, frugal AI, and signal processing for digital communications. It can lead to a number of directions ranging from theoretical machine learning to real-time implementation, depending on the skills and preferences of the candidate.
[FGST23] P. Ferrand, M. Guillaud, C. Studer, O. Tirkkonen, “Wireless Channel Charting: Theory, Practice, and Applications,” IEEE Communication Magazine, 2023.
Principales activités
The successful candidate will join the MARACAS research team of Inria (
The offered post-doctoral contract is for a fixed-term position funded by a collaborative research project. The candidate will have the opportunity to interact and collaborate with our high-profile partners from several high-profile European universities involved in the collaborative project funding the work. The candidate will be supervised by Dr. Maxime Guillaud.
Compétences
The candidate must hold a PhD degree (or must be about to obtain a PhD) in a relevant field such as: computer science, informatics, electronics, mathematics, or statistics.
Required skills:
fluent spoken and written technical english a strong background in machine learning, digital communications, statistical signal processing and/or information theory a solid publication track record familiarity with ScikitLearn, TensorFlow or Keras
Avantages
Subsidized meals Partial reimbursement of public transport costs Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.) Possibility of teleworking (90 days / year) and flexible organization of working hours Professional equipment available (videoconferencing, loan of computer equipment, etc.) Social, cultural and sports events and activities Access to vocational training Social security coverage under conditions
Rémunération
2788€ gross salary / month
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