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  • Graduate Research Intern (Spatial Computing)

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Graduate Research Intern (Spatial Computing)

IRG_M3S_2023_021

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Posted on 23 January 2024
Group: M3S

Project Overview

We are seeking one or more graduate research interns  (ideally full time students currently pursuing a PhD degree) in computer science to join the “Towards an Immersive Workplace” project. This project is part of  Task “T1” of a five-year, ambitious research program on "Mens, Manus and Machina—How AI Empowers People, Institutions and Cities in Singapore (M3S)."

Successful applicants for this position will have the opportunity to work on cutting-edge research that builds new capabilities for AI-enhanced human-robot interaction, with a special focus on supporting efficient and collaborative spatial/3D sensing and comprehension in industrial environments, such as airport tarmacs and logistics warehouses.  The end goal is to build exciting new capabilities that allow robots and humans to work together, interactively and in close proximity, with the spatial sensing helping agents embedded in robots understand and situate human instructions, queries and commands, issued naturally in untethered, field settings.

The research intern, working part-time (10-16 hours per week) will be specifically involved in the development of new, energy-efficient and reliable spatial sensing technologies, such as LIDAR (3D).  The goal is to allow teams of humans and robots, working in close proximity in a common physical environment, to achieve appropriate spatial awareness. The research will focus on addressing challenges such as (i) interference between multiple LIDAR sensors, when operating in a shared physical environment, (ii) optimized sharing of LIDAR-sensed point cloud data among collaborating peers, and (iii) resource-efficient augmentation of LIDAR sensing via use of additional cheaper sensors (e.g., RGB and thermal cameras). These techqniues will progressively be combined with additional research, being developed in complementary efforts within the overall T1 project, that tackles topics such as non-obtrusive human attention capture and efficient multi-modal DNN comprehension for human instructions, to support more accurate understanding of human instructions and preferences, whether explicit or implicit.

The T1 team is led by distinguished scholars, including Profs. Sanjay Sarma, Daniela Rus and Jinhua Zhang from MIT; and Prof. Archan Misra from Singapore Management University. The proposed research described above will be spearheaded by Prof. Misra.

Job Description

  • Conduct research and develop systems-level prototypes that focus on optimized, collaborative and contention-free spatial sensing
  • Publish research results in top-tier conferences and journals, with focus on venues associated with mobile/embedded systems, AI and human robotic interactions.
  • Collaborate with graduate and undergraduate students at MIT and in Singapore, and other SMART post-docs and research engineers

Job Requirements

  • Currently full-time PhD student in Computer Science, with specialization in topics such as Mobile/Wearable Computing, AIoT and/or Multimedia Systems
  • Experience with low-power pervasive systems and applications, with a special focus on spatial sensing and indoor 3D localization  
  • Project-level experience with programming of LIDAR devices and development of 3D point cloud based algorithms is highly desired
  • Strong publication record in top-tier ACM/IEEE conferences and journals.
  • DESIRABLE: Demonstrated ability to conduct inter-disciplinary research with a systems focus.
  • Excellent communication and collaboration skills.

This role is based in Singapore and appointment is on a six-month contract, with the possibility of renewal and extension subject to maintaining full time student status. To find out more about this role, please contact Professor Archan Misra  (archanm@smu.edu.sg). 

Interested applicants are invited to send in their full CV/resume, cover letter and list of one reference. We regret that only shortlisted candidates will be notified.

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