Research Assistant in the Division of Engineering Electrical and Computer Engineering
- NEW YORK UNIVERSITY ABU DHABI
- Abu Dhabi, United Arab Emirates
- 30 Jan 2023
- End of advertisement period
- 16 Feb 2023
- Academic Discipline
- Engineering & Technology, Computer Science
- Contract Type
- Fixed Term
- Full Time
The eBRAIN laboratory in the Division of Engineering at New York University Abu Dhabi invites applications for a Research Assistant position, to work in the area of approximate computing, energy-efficient computing, statistical analysis, high-level synthesis, automated generation of hardware accelerators, and their applications in the emerging autonomous and signal processing systems.
The focus of Prof. Shafique’s eBRAIN lab is on building energy-efficient and robust brain-inspired, autonomous, and cognitive systems through cross-layer analysis and design methods, engaging hardware, software, and system level techniques synergistically. Prof. Shafique’s lab has many-years of R&D experience in cross-layer design and optimization for building energy-efficient and robust AI and vision systems, including efficient learning and inference of complex AI/ML algorithms, specialized neural processing hardware, approximate computing and design tools, and machine learning security, and their applications in resource-constrained embedded AI systems like autonomous vehicles, UAVs, Robotics, and Wearable Healthcare. The long-term vision of the eBRAIN lab is on embedding an energy-efficient and secure electronic brain inside modern cyber-physical systems (CPS) and IoT-Edge devices to enable assistive cognitive technologies that care for / serve humanity and the ecosystem in a safe and green way.
The successful applicant will join and drive a newly accepted 3-years research project on designing, optimizing, prototyping and testing a cross-layer approximate computing framework, and its applications for embedded machine learning, targeting high energy-efficiency. Approximate Computing leverages the inherent resilience of different applications to trade their dispensable fraction of output quality with high energy/performance efficiency. The overarching objective of this project is to increase the energy efficiency of computing systems by orders of magnitude through novel cross-layer concepts/techniques for enabling systematic approximations at multiple system layers, while providing a wide-range of design- and run-time tradeoffs between energy-efficiency and output quality to capture varying application scenarios. This project has a great potential for devising innovative methods to enable next-generation computing systems for Internet-of-Things (IoT), smart industries, smart cyber-physical systems (CPS), robotics, and wearable-healthcare, which run compute-intensive applications from machine learning/AI and advanced signal processing domains under a very tight energy envelop, thereby posing serious energy efficiency challenges.
The candidates will work in a multidisciplinary environment consisting of PhD-level scientists, graduate students and undergraduate students, to investigate cutting-edge scientific methods and to develop full-system prototypes. The eBRAIN lab offers an excellent working environment in an international team with many development possibilities. The lab is fully equipped with advanced prototyping equipment, high-end hardware boards, and software tools. The candidates are expected to work in a highly collaborative environment with other lab members and industry collaborators. There is a great potential to generate high-quality patents and scientific publications from this project.
Applicants should have a Masters Degree (MS, MSc.) or a Bachelor (BS, BSc.) in Computer Engineering, Computer Science, Electrical Engineering, or a related field, along with a strong programming experience. Candidates with an industrial programming experience in C++ and Python are highly desirable. Strong knowledge and experience of programming, machine learning, artificial intelligence, deep neural networks, ML frameworks (like PyTorch and Tensorflow), energy-efficient computing, system-level design and optimization, and hardware design skills (FPGA and/or ASIC) is required. Additional knowledge of statistics, DNN optimization (like pruning and quantization), and AI/ML-System prototyping for autonomous systems is highly desirable.
Applicants with previous publications at top/A* venues (conferences and journals) of these fields are preferred. The candidates are also expected to have strong organization, problem-solving, analytical, communication and writing skills as well as high motivation to pursue world-class research.
The terms of employment are very competitive and include housing and educational subsidies for children. Applications will be accepted immediately and candidates will be considered until the position is filled. For consideration, all applicants must submit a cover letter, curriculum vitae with R&D and programming skillset, list of demos, prototypes, and publications, University degrees and transcripts, or a letter from supervisor/university officials regarding tentative completion date. a one-page summary of research accomplishments and interests, 1-page letter of motivation, stating key technical achievements and interests related to the project and at least 2 letters of recommendation, all in PDF format. One page executive summaries of Master and Bachelor Theses and download links for the PDFs of Master and Bachelor Theses (if available)
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