Smart and Secure Software Engineering for Decentralized Applications

If you share our strong interest in these research topics and if you have

• a master’s in engineering technology (ICT, software development, etc)
• or a master’s in computer science (or equivalent),
• an analytical mind and technically skilled,
• and fluent English communication skills,

then you might be the researcher we are looking for.Expertise in programming languages, software engineering and software development tools is essential. Expertise with system software development or machine learning platforms is a plusWe are seeking 2 motivated and talented PhD candidates to join our team. The successful candidates will contribute to the FWO-SBO research project Sodisa, focused on secure software engineering and development tools for next-generation applications, particularly those leveraging Web3 and blockchain technologies. The goal is to reduce development costs and mitigate security risks across various application domains.Our IDEAS team will focus on developing tools for bug prediction in smart contracts, designed to work with the Solidity programming language for Ethereum, enhancing the security and reliability of smart contracts. Additionally, we will implement software isolation techniques for WebAssembly-based smart contracts to improve their safety and robustness, ensuring secure execution in blockchain environments. By concentrating on these areas, we aim to significantly advance the security and dependability of blockchain technologies.DistriNet is a leading research group at KU Leuven. Our research focuses on distributed software systems and secure software engineering, covering topics such as IoT systems, cloud applications, enterprise software, and data systems. We aim to develop adaptable, high-quality, and trustworthy software systems. IDEAS is the Distrinet applied research unit at Group-T on intelligent and distributed e-application security. IDEAS’ research envisions secure and agile development platforms and methodologies that integrate machine learning (ML) and deep learning (DL) analytics into software engineering processes and artifacts. It also focuses on incorporating usable security concepts and security middleware designed for application developers. This research is being tested and validated across several innovative system architectures, including cloud applications with multi-tenant web APIs, microservice architectures, and decentralized web platforms such as Solid, Web3, and progressive web apps (PWAs).We offer