Research Assistant in Energy-Efficient Streaming Machine Learning at Cloud Edge

Singapore University of Technology and Design

(Senior) Research Assistant

Apply now Job no: 495101
Work type: Contract, full-time
Location: Singapore
Categories: Bachelor Degree, Masters, PhD, Information Systems Technology & Design, Engineering - Electrical & Electronics/Communications, IT - Network/Systems/Database, IT - Software, Others

The Singapore University of Technology and Design (SUTD) is now accepting applications for research assistants in Energy-Efficient Streaming Machine Learning at Cloud Edge. Successful candidates will join an active research group in the ISTD Pillar of SUTD for three years (may concurrently purchase a part-time PhD at SUTD). The candidates will be working on developing novel machine learning model, data mining algorithms, and system optimizations for continuous real-time data streams on asymmetric multicore processors and GPUs.

Project Description:

Distributed machine learning (ML) at network edges is promising that can preserve both network bandwidth and privacy of data providers. However, heterogeneous and limited computation and communication resources on edge devices (or simply edges) pose great challenges on distributed ML. In response, we envision a new paradigm called “green streaming machine learning for edges” (i.e., GSML4E), that continuously perform streaming machine learning tasks in the presence of wirelessly connected edge devices with resource constraints. This project will make large strides towards practical streaming machine learning at edges by developing theoretic methodology and practical technologies for guaranteeing energy-efficient, scalable, and reliability requirements in GSML4E.


  • The candidate must have strong programming skills in C++/Java.
  • Knowledge in streaming machine learning is preferred but not required (can be picked up quickly).
  • Knowledge in database or system development is strongly preferred but not required.
  • Knowledge in one of our open-sourced frameworks is a big bonus (,
  • The candidate must have a solid academic background in Computer Science or Electrical Engineering. Both bachelor and master degrees can apply.
  • Please note that only shortlisted candidates will be contacted.

PI: Shuhao Zhang

Applications close: 26 Aug 2022 Singapore Standard Time

Apply now