Dr Effirul Ramlan is a Lecturer (Above the Bar) at the School of Computer Science, University of Galway. Prior to this role, he served as Genuity Science Lecturer at the UCD School of Medicine, Lecturer in Data Analytics at Ulster University (2019–2021), and Senior Lecturer in the Department of Artificial Intelligence at the University of Malaya (2009–2019). He obtained his PhD in Computer Science from the University of Southampton in 2009, specialising in the computational and theoretical development of nucleic acid computers as information processors.

Dr Ramlan’s research focuses on designing synthetic nucleic acid interfaces, including DNA and RNA nano-machines, capable of molecular-level information processing within biological systems.

He has published over 30 peer-reviewed journal articles and book chapters spanning both life sciences and computer science disciplines. Over the past decade, he has secured funding as Principal Investigator from various national and international research funding bodies.

The science of informed matter has led to the creation of self-organising, adaptive, and complex biocomputers. As a scientist specialising in biological computing, my research is dedicated to developing DNA-RNA interfaces capable of executing complex molecular-level information processing within biological systems.

My pioneering work includes novel molecular fabrication and programming techniques aimed at creating self-organising biomolecular computers. I explore unconventional computational models distinct from traditional silicon-based systems, providing new opportunities for advanced scientific applications, such as amorphous computation for theranostics systems. Additionally, I have developed a suite of computational tools focused on bioinformatics, particularly structural and sequence annotation and analysis. These tools have enabled novel insights into biomolecular structures and functions, thereby contributing significantly to our broader understanding of biological systems.

My research interests extend into evolutionary optimisation and bio-inspired algorithms, integrating artificial intelligence methodologies—particularly machine learning, including foundational models and reinforcement learning—to support diverse research activities in the life sciences. By connecting computer science methodologies with life science applications, my work seeks to drive groundbreaking discoveries in key areas, such as drug discovery, medical image analysis, and disease diagnosis.

Module Coordinator for:

• CT417 - Software Engineering III
• CT413 - Final Year Project (CS)
• CT2101 - Object Oriented Programming I
• CT5168 - Enterprise Java Programming
• CT5178 - Computing Architecture & Operating Systems
• CT3112 - Professional Skills