About the SnT The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Interdisciplinary Centre for Security, Reliability and Trust ( SnT ) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role in Europe by fueling innovation through research partnerships with industry, boosting RandD investments leading to economic growth, and attracting highly qualified talent.Welook forresearchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services & Applications. Your role The successful candidate will join the Serval research group and work on a large research project related to Machine Learning Security and Testing. The project involves the study of technical methods and approaches for testing the reliability (in particular, the security) of machine learning systems (including generative AI and large language models) against various threats that may occur in the real world. Successful candidates will extensively explore and develop software security and testing techniques for machine learning systems. These investigations include the feasibility, practicality and success evaluation of prototype implementations. We expect the candidate to publish in competitive machine learning and security venues, such as Neurips, ICML, ICLR, Security & Privacy, USENIX Security, CCS, etc. More generally, the project is part of a large initiative at Serval and SnT, which aims to support the reliable deployment of machine learning systems by providing industry actors with practical evaluation tools, such as technical testing platforms for AI. The position holder will be required to perform the following tasks/will do research on the following topics: Quality assurance practices for machine learning Define quality and security principles for machine learning systems Design and implement technical testing algorithms to assess these systems in real world conditions. Under the direction of their supervisor, the candidate will carry out research activities, including tool development, and write research papers in the area of machine learning. This includes conducting literature surveys and establishing state of the art; developing necessary experimental and simulation facilities where required; planning, executing, and analyzing experiments and simulations; conducting joint and independent research activities; contributing to project deliverables, milestones, demonstrations, and meetings; disseminating results at international scientific conferences/workshops and peer reviewed scientific publications. For further information, please contact Dr. Maxime Cordy Your profile PhD in Computer Science, w