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Position: Doctoral Candidate (PhD student) in ML-based radio resource management for 6G wireless networks
Institution: University of Luxembourg
Location: Luxembourg City, Luxembourg
Duties: This is a fully funded position for 3 years (extendable to an additional 4th year) within the European project PASSIONATE- Physics-based wireless AI providing scalability and efficiency that will start on 01/01/2024. PASSIONATE comprises 6 partners and will pave the way to a complete change of paradigm in the way AI/ML is applied to wireless communications, developing physics-based native AI tools and applying them to the design of innovative PHY, MAC, and RRM techniques. The term “physics-based” AI refers to the combination of physical modelling based on numerical simulations or mathematical models with methods based on AI. The general direction of physics-based (or physics-informed) AI represents a very active, quickly growing, exciting but little understood field of research. In the context of PASSIONATE, the role of the recruited PhD candidate will be the development of AI/ML solutions for the resource and traffic management of 6G wireless networks that will leverage their inherent structure
Requirements: The candidate should possess an M.Sc./M.Eng. Degree in Telecommunication Engineering, signal processing, or a closely related field in Electronic, Electrical and Computer Engineering. The ideal candidate should have a strong background in signal theory and digital communications, substantiated by relevant coursework/assignments
   
Text: Doctoral Candidate (PhD student) in ML-based radio resource management for 6G wireless networks This is a fully funded position for 3 years (extendable to an additional 4th year) within the European project PASSIONATE- Physics-based wireless AI providing scalability and efficiency that will start on 01/01/2024. PASSIONATE comprises 6 partners and will pave the way to a complete change of paradigm in the way AI/ML is applied to wireless communications, developing physics-based native AI tools and applying them to the design of innovative PHY, MAC, and RRM techniques. The term “physics-based” AI refers to the combination of physical modelling based on numerical simulations or mathematical models with methods based on AI. The general direction of physics-based (or physics-informed) AI represents a very active, quickly growing, exciting but little understood field of research. In the context of PASSIONATE, the role of the recruited PhD candidate will be the development of AI/ML solutions for the resource and traffic management of 6G wireless networks that will leverage their inherent structure The candidate should possess an M.Sc./M.Eng. Degree in Telecommunication Engineering, signal processing, or a closely related field in Electronic, Electrical and Computer Engineering. The ideal candidate should have a strong background in signal theory and digital communications, substantiated by relevant coursework/assignments
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