Doctoral Candidate (PhD) in Computer Science / Software Engineering on Diff

at  Universit du Luxembourg

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 R&D investments leading to economic growth, and attracting highly qualified talent. We look for researchers 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.

Successful candidate will join the young, vibrant, and interdisciplinary FINATRAX Research Group, which builds bridges between business research and information systems engineering. The group conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, Machine Learning/AI, and IoT/5G on organisations from both the private and public sectors. The group consists of doctoral and post-doctoral researchers from diverse backgrounds. For more information, please visit our website: https://wwwen.uni.lu/snt/research/finatrax/projects
Successful candidate will pursue a Ph.D. degree (Doctorate) in computer science or software engineering with a focus on differential privacy (DP) and other secure computing techniques while collaborating with the Ministry for Digitalization. This project aims to advance privacy-preserving techniques for data analysis and task automation, ensuring robust protection of sensitive information. The focus will be on developing and evaluating differential privacy frameworks, as well as secure computing techniques to enable secure, private, and scalable data-driven systems for the secure and privacy-preserving utilization of public data. Key challenges include balancing privacy guarantees with utility, designing efficient algorithms for real-world applications, and assessing security and business implications in practical settings.

In general, the candidate will perform the following tasks:

• Carry out research in areas relevant to differential privacy, such as:
• Implementing novel differential privacy mechanisms for data analysis and automation tasks
• Benchmarking state-of-the-art differential privacy techniques with respect to cost, performance, and utility
• Developing architectures and tools for privacy-preserving task automation
• Exploring machine learning in privacy-sensitive settings
• Investigating trade-offs between explainability, privacy, and system performance
• Disseminate results through scientific publications in leading outlets at the intersection of privacy, information systems, and computer science
• Support the conceptualization and writing of research proposals to attract industry partnerships and secure national and European grants
• Conduct research projects and and deliver project outcomes in collaboration with academic and public sector partners

For further information, please contact Afshin Khadankishandi (afshin.khadanki@uni.lu) and Gilbert Fridgen (gilbert.fridgen@uni.lu).