PhD in Federated Unlearning for Industrial Internet of Things

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 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.

The SigCom research group of SnT, headed by Prof. Symeon Chatzinotas, focuses on wireless/satellite communications and networking. The research areas focus on the formulation, modeling, design, and analysis of future 6G communication networks that are capable of supporting new services for digital ecosystems. Use cases of interest include Security and Efficient Wireless Communication Solutions, IoT verticals, Unmanned Aerial Vehicles, Integrated Satellite-Space-Terrestrial Networks, Quantum Communications and Key Distribution, Spectrum Management and Coexistence, Tactile Internet, Earth Observation, and Autonomous Transportation. We leverage expertise on advanced technologies including semantic/task-oriented data processing, signal processing, network resource management to improve the performance of the future wireless communication systems. Finally, due to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Our activities are experimentally driven and supported by the COMMLab, the 6GSPACE Lab, the HybridNetLab, the QCILab, our SW Simulators, and our Facilities. For further information, you may refer to https://www.uni.lu/snt-en/research-groups/sigcom/.

Your role

This is a fully funded position for 3 years (extendable to an additional 4th year) within a cutting-edge research project focusing on Federated Unlearning for Industrial Internet of Things (IIoT). The PhD project aims to develop a novel framework for federated unlearning, enabling the selective removal of data contributions in decentralized machine learning systems. This approach addresses critical challenges in data privacy, compliance, and dynamic system adaptation, which are essential for the evolution of Industry 5.0. The research will explore innovative techniques to empower IIoT systems with adaptive intelligence while ensuring data privacy and regulatory adherence. By leveraging federated learning methodologies and advanced unlearning strategies, the project seeks to create a robust and scalable solution that can operate efficiently in resource-constrained environments, such as edge devices and embedded systems. The ultimate goal is to design an architecture that optimally utilizes distributed and hybrid computational resources, enabling a secure and intelligent IIoT ecosystem capable of dynamic decision-making, energy efficiency, and resilience.

The successful candidate will work under the academic supervision of Prof. Symeon Chatzinotas, Dr. Ons Aouedi, and Dr. Konstantinos Ntontin and join a strong and motivated research team.

You will be required to perform the following tasks:

• Carrying out research in the predefined areas
• Disseminating results through scientific publications
• Present results in well-known international conferences and workshops
• Participating in outreach events related to Federated/machine Learning, inspiring young generations to be interested in related topics

For further information, please contact Dr. Ons Aouedi ().

Your profile

Qualification: The candidate should possess an MSc degree or equivalent in Telecommunications and Computer Science.

Experience: The ideal candidate should have some knowledge and/or experience in several of the following topics:

• Strong Knowledge of Machine Learning, specially, distributed AI and Federated Learning, with hand-on implementation experience
• Strong analytical, problem solver, and programming skills (Python) are preferred
• Background in general wireless communications and optimization techniques and tools will be considered as an advantage

Prior proven experience in data-driven innovation projects is an asset as well as having prior research publications in high-ranking journals or in international conference proceedings. Most importantly, you should be curiosity driven and willing to constantly learn new things!

Language Skills: Fluent written and verbal communication skills in English are required.

We offer

• Multilingual and international character. Modern institution with a personal atmosphere. Staff coming from 90 countries. Member of the "University of the Greater Region" (UniGR)
• A modern and dynamic university. High-quality equipment. Close ties to the business world and to the Luxembourg labour market. A unique urban site with excellent infrastructure
• A partner for society and industry. Cooperation with European institutions, innovative companies, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs …

How to apply

Applications should include:

• Full CV, including:
• For each degree received or currently enrolled in, the degree, institution name, city, and country, and date (or expected date) of graduation
• Title and short summary of final thesis
• List of publications (if any)
• Name, affiliation, and contact details of at least two recommenders
• If available, link to GitHub repository including completed open-source projects

• Transcript of all modules and results from university-level courses taken
• Cover letter with motivations and topics of particular interest to the candidate (500 words)

Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by Email will not be considered.

All qualified individuals are encouraged to apply. In line with our values, the University of Luxembourg promotes an inclusive culture. We encourage applications from individuals of all backgrounds and are dedicated to upholding equality and respect for our employees and students.

General information:

• Contract Type: Fixed Term Contract 36 Month (extendable up to 48 months if required)
• Work Hours: Full Time 40.0 Hours per Week
• Location: Kirchberg Campus
• Internal Title: Doctoral Researcher
• Job Reference: UOL07008

The yearly gross salary for every PhD at the UL is EUR 40952 (full time).