Job Description

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.

We are seeking a highly motivated and talented Research Fellow to join an exciting applied research project within the Infocomm Technology cluster at SIT. This project addresses the critical challenge of delivering real-time Artificial Intelligence (AI) services in Internet of Things (IoT) networks, where latency and timeliness are paramount.

The successful candidate will be responsible for the end-to-end investigation of novel edge-assisted offloading strategies for IoT networks. The role will bridge rigorous theoretical work with hands-on offloading algorithm design and development for IoT networks. The core responsibility is to build and validate edge-assisted offloading strategies, complete with software APIs, through rigorous simulations and live demonstrations.

This position is ideal for a researcher with a passion for solving complex problems at the intersection of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical, IoT systems.

Key Responsibilities

• Participate in and manage the research project with Principal Investigator (PI) to ensure all project deliverables are met.
• Derivation of closed-form theoretical latency and timeliness expressions for cloud-hosted AI services and edge-assisted offloading strategies.
• Analysis of theoretical latency and timeliness for cloud-hosted AI services and edge-assisted offloading strategies.
• Design and development of edge-assisted offloading strategy and associated software APIs.
• Validation of edge-assisted offloading strategy via simulations and live demonstrations.

Job Requirements

• A Ph.D degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent.
• Independent, highly analytical, proactive and a team player.
• Strong theoretical background in wireless communications or edge computing will be advantageous.
• Proven track record in research and development of edge intelligence algorithms will be advantageous.
• Knowledge of machine learning or reinforcement learning techniques will be advantageous.
• Proficiency in algorithm development using Python will be advantageous