A postdoctoral position is available at United Arab Emirates University in the Departments of Electrical and Communication Engineering, focusing on embedded systems and artificial intelligence for biomedical applications. The research aims to develop machine learning edge accelerators for wearable health monitoring devices, including the design and implementation of embedded systems for recording vital signals such as EEG, PPG, and ECG. The project seeks to enable real-time health monitoring and on-device machine learning, optimizing hardware accelerators for wearable health sensors under strict power and size constraints. The successful candidate will contribute to innovative healthcare solutions, including mental health monitoring, and will participate in advising MSc and PhD students as well as manuscript writing for scientific journals and conferences.

Applicants must hold a PhD or equivalent in electrical engineering, computer engineering, or computer sciences, with prior experience in machine learning, embedded systems, and edge devices. A strong research background in biomedical engineering using embedded systems and machine learning is essential, along with a proven publication record in relevant IEEE journals and conferences. Hands-on experience with MCUs and FPGAs, as well as designing and synthesizing AI models on FPGA or deploying AI models on MCUs, is required. The position offers a competitive salary according to international standards and UAEU regulations, opportunities to participate in international conferences, and access to a strong international scientific network within a supportive and multicultural environment.

Interested candidates should apply online via the provided application link and submit a detailed CV before the closing date of May 31, 2026. The position is supervised by Professor Mohamed Atef and is based in Abu Dhabi, United Arab Emirates. Research keywords include embedded systems, artificial intelligence, biomedical applications, machine learning, edge accelerators, and wearable health monitoring.