Nadeem Rather

Nadeem Rather is a Research Fellow at the Wireless Communications and AI Research Lab, Tyndall National Institute, University College Cork (UCC), Ireland, where he has conducted research since 2019. His work focuses on developing AI-driven electromagnetic systems with emphasis on RF technologies and intelligent antenna design.

His academic background includes:

• Bachelor’s degree in Electronics and Communication Engineering
• Research Master’s degree in Communication Systems
• PhD from University College Cork (2024)

Dr. Rather’s research integrates advanced AI algorithms with electromagnetic systems to optimize RF components for emerging IoT applications. His work spans chipless RFID, NFC sensor networks, and adaptive antenna design, addressing challenges in spectrum efficiency, robust detection, and practical implementation across agriculture, healthcare, and cultural heritage domains. He bridges theoretical electromagnetic modeling with real-world sensor deployment.

His publication trend reveals a concentrated focus on machine learning for electromagnetic systems, particularly in chipless RFID and NFC applications. Recent works demonstrate innovative approaches to spectrum utilization, tag design, and robust detection techniques using deep learning architectures like U-Net, with significant contributions to agricultural monitoring, museum preservation, and animal health tracking.

His scientific achievements have been recognized through:

• Best Application of AI in a Student Project (AI Awards Ireland)
• Wrixon Research Excellence Bursary
• Postgraduate Publication of the Year Awards (2020, 2023)
• Best Student Research Paper, International Workshop on Antenna Technology (2021)
• Best Demonstration Award, VistaMilk Conference (2023)

As an active IEEE Member, Dr. Rather contributes to the Wireless Communications and AI Research Lab’s mission of advancing electromagnetic systems through AI. His current projects include transparent epidermal antennas for human-centric IoT and battery-less NFC sensors for health monitoring, with demonstrated impact in cattle health tracking and museum artifact preservation.