Posting Start Date: 20/03/2026

Schemes of Service: Research

Division: Infocomm Technology

Employment Type: Fixed Term

About the University

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.

The Role

We are seeking a highly motivated and talented Research Fellow to join an exciting industry-funded applied research project within the Infocomm Technology cluster at SIT. This project addresses the critical challenge of enabling ultra-reliable low-latency communications (URLLC) with priority-aware offloading for public safety services.

The successful candidate will be responsible for the end-to-end investigation of novel edge-assisted computation offloading strategies that leverages edge intelligence. The role will bridge rigorous theoretical work with hands-on offloading algorithm design and development. The core responsibility is to build and validate these offloading strategies, complete with Python application programming interfaces (APIs), through rigorous simulations and proof-of-concept (PoC) trials.

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 systems.

Key Responsibilities

• Derive and analyse closed-form mathematical expressions for latency and offloading probabilities across edge-only, cloud-only, and edge-cloud computation offloading algorithms.
• Design and develop robust Python APIs for edge-only, cloud-only, and edge-cloud computation offloading algorithms.
• Set up network testbeds integrating hardware, software, and communication protocols to validate edge-only, cloud-only, and edge-cloud computation offloading algorithms.
• Conduct rigorous simulations and live PoC trials to ensure computation offloading algorithms meets URLLC targets for public safety services.
• Participate in and manage the research project with Principal Investigator (PI) to ensure all project deliverables are met.

Job Requirements

A Ph.D./Master’s degree in Computer Engineering, Communications Engineering, Computer Science, or a highly related discipline.

A strong theoretical foundation and research background in wireless communications and edge computing.

Strong publication track record in top-tier conferences and journals in the areas of wireless communications, IoT, or edge computing.

Demonstrated proficiency in software API and algorithm development of edge intelligence algorithms using Python.

Knowledge of machine learning or reinforcement learning techniques is highly advantageous.

Experience with theoretical wireless network modelling, particularly stochastic geometry or queuing theory, is highly advantageous.