We are always looking for dedicated students and researchers to join our lab!
GWU's unparalleled location and highly collaborative approach has encouraged students, faculty and staff to cultivate expertise in laboratories and influence in government. GWU is a founding member of the National Science Foundation's Center for High-Performance Reconfigurable Computing and a top source for the government's cybersecurity policy and research. GWU relishes opportunities to work with the world's most powerful institutions on discoveries and breakthroughs that impact everyone's lives.
Science and Engineering Hall, the largest academic building dedicated to these fields in the city, is our hub for discovery and provides countless opportunities for collaboration among researchers and students.
Professor Louri and his team investigate novel parallel computer architectures and technologies which deliver high reliability, high performance, and energy-efficient solutions to important application domains and societal needs. The research has far-reaching impacts on the computing industry and society at large. Current research topics include: (1) the use of machine learning techniques for designing energy-efficient, reliable multicore architectures, (2) scalable accelerator-rich reconfigurable heterogeneous architectures, (3) emerging interconnect technologies (photonic, wireless, RF, hybrid) for network-on-chips (NoCs) & embedded systems, (4) future parallel computing models and architectures including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), near data computing, approximate computing, and (5) cloud and edge computing.
A postdoctoral position is available at HPCAT Lab at GWU. This position is primarily a temporary/training position in which the incumbent plays a substantive role in planning and conducting research by using quantitative and qualitative methods to collect, analyze and report data activities. In collaboration with the Principal Investigator, this role will participate in the planning of independent research, will analyze and interpret data, will publish results, will represent the university at conferences and meetings, and may develop new theories and methodologies. This position may also help the Principal Investigator to lead and direct the work of lower level research staff. This role performs work under the supervision of experienced researchers.
Specific Duties For The Position Include:
*The use of machine learning techniques for designing energy-efficient, reliable and secure multicore architectures.
*Design of scalable accelerator-rich reconfigurable heterogeneous architectures.
*Exploration of emerging interconnect technologies (photonic, wireless, RF, hybrid) for network-on-chips (NoCs) & embedded systems.
*Design of accelerators for convolutional neural networks (CNNs), deep neural networks (DNNs), graph convolutional networks (GCNs).
*Design of approximate communication systems for parallel computing.
*Performs other related duties as assigned. The omission of specific duties does not preclude the supervisor from assigning duties that are logically related to the position.
*Qualified candidates will hold a Doctoral degree in a related discipline. Degree must be conferred by the start date of the position.
*Preferred PhD degree in Electrical Engineering, Computer Engineering or Computer Science, or related discipline and whose research focus is on the mainstream computer architecture and parallel processing.
*Experience as well as publications in leading conferences and journals on computer architecture.
Graduate Research Assistantships (GRAs) are available for Ph.D. students to work on several federally funded research projects. Students in the HPCAT Lab will work on research topics including, but not limited to energy-efficient, reliable, and high-performance many-core architectures; accelerator-rich reconfigurable heterogeneous architectures; machine learning techniques for efficient computing and interconnect systems; emerging technologies (photonic, wireless, RF, hybrid) for NoCs; graph processing and neural network accelerators; accelerating machine learning with approximation; heterogeneous chiplet-based architectures; application-aware reconfigurable manycore architectures; security in computer architecture; future parallel computing models and architectures (including CNNs, DNNs, GCNs, and approximate computing); and cloud-computing for data centers, etc.
Students will gain a well-rounded experience in computer & communication systems design through solid theoretical studies, modeling and simulation experiments, performance evaluation and physical implementation. A variety of modeling and simulation tools as well as equipment are available for use in the Lab. Knowledge of basic computer architecture and programming skills are a plus. Successful candidates should be motivated and willing to learn new concepts and be at the forefront of cutting-edge-research in this exciting field.
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