Research Assistant in IT Security and Explainable AI
vor 4 Wochen
Position Overview
The Chair of "Artificial Intelligence & Security" is engaged in high-level research at the convergence of cybersecurity and artificial intelligence. Our focus is on developing innovative, learning-based techniques for detecting attacks and identifying vulnerabilities within software systems. Additionally, we investigate the robustness and security aspects of artificial intelligence methodologies.
Research Focus Areas
Explainable AI (XAI) plays a pivotal role in our research, particularly in attack detection across both domains: the security of AI systems and the application of AI in enhancing IT security. Key research activities include:
- Identifying and mitigating attacks on computer systems, including malware analysis and vulnerability discovery through fuzz testing.
- Detecting threats to machine learning models, such as backdoor attacks, adversarial examples, and model extraction.
Furthermore, ensuring the resilience of XAI methods against potential attacks is of utmost importance. Our research also emphasizes:
- Manipulations of inputs or models to obscure attack vectors.
- Addressing biases in XAI outputs.
- Developing certifiably robust XAI methodologies.
Candidate Profile
We are looking for outstanding candidates for the role of Ph.D. student or research assistant to engage in pioneering research. Successful candidates will become part of the "KIT Graduate School Cyber Security." Our research group boasts strong partnerships and collaborations with both national and international research institutions. We maintain our own server and cluster infrastructure to optimally support our research endeavors and provide our team members with top-tier hardware.
Qualifications
- A Master's degree in computer science or a related discipline.
- Strong knowledge in the fields of computer security and/or machine learning.
- A genuine passion for exploring advanced topics in cybersecurity and artificial intelligence, along with a readiness to tackle complex challenges.
Compensation
Salary will be categorized under TV-L 13, contingent upon meeting professional and personal criteria.
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