Develop machine learning-based solutions to secure UAV-enabled communication networks at the physical layer, addressing challenges such as high mobility, dynamic topologies, and intermittent connectivity; Investigate and implement adversarial training frameworks to protect machine learning models from intentional threats, including jamming targeting UAV communication channels; Develop and implement a software and hardware testing environment for UAV. communication systems, enabling real-time validation of proposed techniques; Utilize platforms like USRP/SDR-based testbeds to demonstrate system feasibility and performance
Requirements:
A PhD degree in Telecommunication Engineering, or Electrical Engineering or Computer Science/engineering, with a focus on wireless communications, physical layer security, and AI/ML methods including adversarial training; Proven expertise in physical layer security, particularly in UAV communications, addressing challenges such as mobility, Doppler effects, and fast-fading environments; Practical experience in applying adversarial training or other robustness techniques to improve the security and reliability of machine learning models
Text:
Research Associate (Postdoc) in Machine Learning Optimization for Secure 6G Physical Layer Develop machine learning-based solutions to secure UAV-enabled communication networks at the physical layer, addressing challenges such as high mobility, dynamic topologies, and intermittent connectivity; Investigate and implement adversarial training frameworks to protect machine learning models from intentional threats, including jamming targeting UAV communication channels; Develop and implement a software and hardware testing environment for UAV. communication systems, enabling real-time validation of proposed techniques; Utilize platforms like USRP/SDR-based testbeds to demonstrate system feasibility and performance A PhD degree in Telecommunication Engineering, or Electrical Engineering or Computer Science/engineering, with a focus on wireless communications, physical layer security, and AI/ML methods including adversarial training; Proven expertise in physical layer security, particularly in UAV communications, addressing challenges such as mobility, Doppler effects, and fast-fading environments; Practical experience in applying adversarial training or other robustness techniques to improve the security and reliability of machine learning models
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