Research Associate at the Institute of Software Security
Role details
Job location
Tech stack
Job description
The Institute of Software Security at Hamburg University of Technology is hiring a graduate research associate to work in the DFG-funded project "DevSSATD: Developer-centered Management of Self-Admitted Technical Debt for Security." The project seeks to be at the forefront of technical debt security research by combining human-centered research with code analysis and Mining Software Repositories (MSR) techniques. Do these topics resonate with you and your prior technical experience? Would you like to explore them as part of a PhD project in close collaboration with researchers across Europe and Australia? A central focus is the analysis of the security implications of technical debt in software-intensive systems and the development of usable technologies to enable its early identification, assessment, and remediation. Special attention will be given to security debt arising from generative Al-augmented software systems. The project also foresees close collaboration with industrial partners to ensure the practical relevance of the research outcomes.
YOUR TASKS
- Conduct in-depth security analysis of software artifacts (e.g., source code and code comments)
- Carry out human-centered studies and controlled experiments with software developers
- Develop usable Al-based solutions that address security debt
- Compile and present the results at international conferences and publish in academic journals
Requirements
Do you have experience in Machine learning?, Do you have a Master's degree?, * Completed scientific university studies (masters degree or equivalent), in particular in the subject Software Engineering or Information Systems, * Very good English language skills (C1)
- Expertise in software security (e.g., run and interpret static code analysis outputs)
- Proficiency in empirical research methods and data analysis (e.g., MSR, controlled experiments, and surveys)
- Proven knowledge and experience on Machine Learning and Artificial Intelligence