Research Assistant / PhD Student (f/m/d) in the field of Security and Robustness of Machine Learning
Institute of Information Security and Dependability (KASTEL)
The "Intelligent System Security" research group works at the intersection of machine learning and computer security. We develop learning-based methods for attack detection on different levels or the discovery of vulnerabilities in software. Moreover, we research the robustness and security of machine learning methods themselves. Here, we are particularly looking for reinforcing our team.
Possible research topics include, but are not limited to:
- Attacks against learning-based systems
- ML backdoors, adversarial examples, model stealing, ...
- Secure and robust learning methods
- Adversarial training
- Explainability of machine learning in computer security
zum nächstmöglichen Zeitpunkt / as soon as possible
You have a Diploma or Master's degree in computer science or any related field. You also require very good knowledge in machine learning and ideally computer security. Above all, however, you need to show enthusiasm for conducting research on cutting-edge topics in machine learning and computer security.
The remuneration occurs on the basis of the wage agreement of the civil service in TV-L E13, depending on the fulfillment of professional and personal requirements.
limited for 18 months (While the position is limited at first, extending the contract to the entire duration of the PhD is possible and intended.)
Application up to
November 15, 2021
Contact person in line-management
For further information, please contact TT.-Prof. Christian Wressnegger, e-mail: firstname.lastname@example.org.
Please send your application including a cover letter, your CV, and all certificates/referees and a research statement in electronic form to: email@example.com.
Make sure to point out why you are a good fit for us and research in the field of IT-Security.
vacancy number: 2145/2021
We prefer to balance the number of employees (f/m/d). Therefore, we kindly ask female applicants to apply for this job.
Recognized severely disabled persons will be preferred if they are equally qualified.