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The eBRAIN laboratory in the Division of Engineering at New York University Abu Dhabi invites applications for multiple Research Associate / Postdoctoral Associate positions, to work in the area of Edge-AI and embedded machine learning, with a focus on energy-efficiency, robustness and online learning, and their applications in the emerging autonomous systems.
The focus of the 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 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 applicants will join and drive a number of fascinating projects on designing, optimizing, prototyping and testing conjoint hardware and software techniques for embedded machine learning, targeting high energy-efficiency, robustness, and continual learning. These projects have a great potential for devising innovative methods to enable next-generation embedded AI features for autonomous systems, assistive robotics, and smart energy & industrial sectors. 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 candidates are expected to work in a highly collaborative environment with other lab members and industry collaborators.
Requirements: Applicants should have a PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related field. Extensive knowledge of 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), security for ML, 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 highly 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.
Application Procedure: 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. To be considered, all applicants must submit the following documents, all in PDF format.A cover letter Detailed curriculum vitae (CV) with R&D and programming skillset Full list of publications University degrees and transcripts, or a letter from supervisor/university officials regarding tentative completion date. 1-page summary of research accomplishments and interests 1-page letter of motivation At least 2 letters of recommendation with contact information of the referees Research statement with an envisioned workplan over 2 years 1 page executive summaries of Master and PhD Theses Download links for the PDFs of Master and PhD Theses (if available)
Please visit our website at http://nyuad.nyu.edu/en/about/careers/faculty-positions.html for instructions and information on how