Post Doctoral Fellow, Electrical and Computer Engineering
Organization Name Electrical and Computer Engineering
The newly-established Khalifa University of Science and Technology (KU) combines the Masdar Institute of Science and Technology (MI), the Khalifa University of Science, Technology and Research (KUSTAR) and the Petroleum Institute (PI) into one world-class, research-intensive institution, seamlessly integrating research and education to produce world leaders and critical thinkers in applied science and engineering. Khalifa University endeavors to be a leader among research intensive universities of the 21st century, while catalyzing the growth of Abu Dhabi and the UAE’s rapidly developing knowledge economy.
Artificial Intelligence (AI) solutions are taking the lead in pioneering research to develop the next generation technologies. Next stage of AI systems promises to have improved robustness and reliability; enhanced security; reduced power, data, and performance inefficiencies; and enable common sense reasoning. AI applications include but not limited to smart healthcare systems, self-driving cars, robotics, etc. Conventional CMOS technology which has provided cheaper, faster, and lower power electronics system over the past 40-years is facing big challenges. In addition, von Neumann architecture does not match the characteristics of machine learning algorithms, hence researchers are looking into novel technology and hardware architectures for accelerating such algorithms.
In this project, DeepMem, an efficient intelligent Memristor-based hardware system is introduced. Memristor technology enables efficient in-memory computations with low power and high density. Co-optimization of machine learning algorithms as well as Memristor-based in-memory computing for IoT devices is the main aim of this project.
The Post - Doc will be part of a research group to investigate memristor technology for computing. The duties and responsibilities may include (as required) extensive research and investigation at four different levels:
- Design and fabrication of Memristor devices that can provide the required area, power consumption, speed and resistance ranges.
- Efficient mapping of the convolutional neural network stages to the Memristor crossbar operations to achieve minimal power and storage arrays.
- Algorithm optimization to match memristor architecture characteristics.
- Manage the system architecture and interconnected circuits especially Memristor read/write operations.
- Integration of both digital (CMOS) with memristor based design
PhD Degree in Electrical and Electronics Engineering.
Thorough experience with RRAM technology, modeling and simulation is required. Understanding of machine learning algorithms and artificial intelligence is essential. Experience with MATLAB and spice modeling environments. Excellent writing and communication skills.
How To Apply
Should you require further assistance or if you face any issue with the online application, please feel to contact the Recruitment Team (RecruitmentTeam@ku.ac.ae)