5G Baseband/Physical Layer Algorithm Researcher, Swedium Global Services AB
Swedium the growing is Global System Engineering and Solution Company, offers services like Engineering R & D Services, Embedded Systems, custom application development, Onsite Consultancy and Testing Services to clients across the globe for onsite and offshore business model. We provide industry solutions to our customer through our dedicated development center in Bangalore (India) and Stockholm (Sweden).
We are looking for 5G Baseband/Physical Layer Algorithm Researcher
Algorithm Lab in Sweden drives innovation for the Client's wireless Radio Access Networks (RAN) product. We work on physical layer signal processing and Radio Resource Management (RRM) algorithms.
The research is in nontraditional areas and we cooperate with universities all over Europe to find the next technological breakthroughs. If this sounds like an interesting challenge where your background will be appreciated, then you should consider the following positions.
• Research and innovation in the area of baseband/physical layer algorithm for 5G product.
• To follow and influence the research tracks in client's wireless RAN.
• Some travelling is expected, both within Europe and to China.
• A M.Sc. degree in Electrical Engineering or equivalent.
• Experience in technologies like MIMO, OFDM.
• Experience in modeling and simulation.
• Knowledge of C/C++.
• A PhD degree in Electrical Engineering or equivalent.
• Solid research background in Massive MIMO and mm-Wave.
• Up-to-date knowledge about new research trends in academia.
Optional requirements for experienced candidate:
• 3Gpp RAN 1 experiences, Research experience (not development experience) in physical layer algorithm, MIMO, OFDM, precoding, FDD massive MIMO Optional requirements (especially for new graduates):
• Massive MIMO signal processing, Matrix theory, mmWave precoder, receiver filter design, channel reconstruction, channel tracking,Statistic signal processing,Belief propogation/message passing, Factor graph, Baysian estimation, Compressed sensing,Markov chain, Spatial-temporal processing, Machine learning with CNN, RNN, auto-encoder