International Association for Cryptologic Research

International Association
for Cryptologic Research

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Luxembourg Institute of Science and Technology
Are you passionate about research? So are we! Come and join us The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. Discover our IT for Innovative Services department: How will you contribute? Your specific mission includes, but is not limited to, participating into the following activities along the project partners: • to design and develop DevSecOps solutions and Data security solutions • to prototype ML-based anti-fuzzing, vulnerability detection, information sharing technologies for cybersecurity, and anomaly detection solutions • to develop open-source software • to validate the effectiveness of developed technologies You are in charge of disseminating and promoting the research activities that will be carried out, whether through publications, prototype development or technical reports. You’re highly motivated and have proven skills in machine learning & cybersecurity to address the security concerns in software development and data protection. You have already good experience in collaborative cyberthreat intelligence systems that use advanced analytics solutions as can offer significant advantages over the local systems by detecting cyberattacks early and promptly responding to them. And last, but not least, you’re a great practitioner of cybersecurity techniques such as vulnerability detection, information sharing, fuzzing, anti-fuzzing. As to join us, you: • hold a PhD. degree in Computer Science or related disciplines • have good programming skills (particularly experience on Python and C++) • have good track record on applied ML for cybersecurity, such as fuzzing and ML-based vulnerability detection and anomaly detection. • have good track record on data protection for applications such as data spaces and digital twin. • demonstrate strong interest and experience in fuzzing techniques such as mutation testing, symbolic execution, AFL, etc. • familiar with ML frameworks, such as Pytorch, Tenso
Last updated: 2024-04-17 posted on 2024-04-16