Communication networks, such as wireless ad hoc or cellular networks, are being increasingly used for computation purposes. Representative examples are over-the-air computing, computing via gossip motivated by sensor networks, holoportation, and interactive communication. When the functions of interest are linear, in special instances, there exist algorithms to efficiently solve the problem. Computation tasks such as ranking of sources, and compressive sensing across networks, or even modeling the link delay or the probability of outage, as well as precoding for efficient data transmission, are only but a few of the many examples of nonlinear functions of interests over communication networks. For executing them, while parallel computing or replication-based techniques, e.g., MapReduce, and scheduling or pipelining have been exploited, physical constraints, such as bandwidth, power, and routing complexity, hinder their scalability. Devising low-complexity algorithms is formidable as existing coding principles cannot be simply extended to nonlinear computing scenarios. We envision a distributed framework for computing functions of data over communication networks. Our objective is to create a unified framework for distributed function computation in networks. Looking beyond the current research horizon, we envision a radically new approach to design our framework which involves a careful balance between data, function, and network. We target the emerging frontier research field of distributed functional compression over networks, which capitalizes on finding the shortest length explanation of a function in the number of exchanged communication bits over a network.
In this thesis, the PhD student will take a deeper look at distributed computing problems over wireless networks. The student will develop a background on the theoretical limits of communications when communication networks are tailored for performing specific tasks. Consider a scenario where distributed sensors collect the temperature information from a large geographic region. Their goal is to send their local observations to a common access point whose objective is to decide whether the temperature is above a critical threshold, e.g., heat wave. One trivial way for making this decision is via letting each sensor to transmit the entire information, which of course is a redundant approach from an energy efficiency perspective, and at the same time vulnerable to malicious users (if any). We seek ways of compressing the redundant information among dispersed sensors in a robust manner by exploiting the structural correlations between their measurements.
More specifically, the goal of this PhD thesis is to design low complexity coding techniques for computation over communication networks. This research area brings together tools from information theory and graph theory, and has applications in in edge/cloud computing scenarios, large language models, task-oriented communication and learning, fundamental limits of computation, decentralized and federated learning, intelligent communication systems, and sensing and connected robotics.
The PhD position is as part of a HUAWEI grant on Advanced Wireless Systems with a focus on fundamental limits of distributed computation over networks and computation-communication-learning tradeoffs. The position is intended for talented researchers with the drive to push the knowledge frontiers in the area of advanced wireless networks.
Requirements
- Education Level / Degree: Masters and Undergraduate degree in Electrical Engineering or in Mathematics
- Field / specialty: Mathematics, Electrical Engineering, Computer Science, Information Theory
- Other skills / specialties: Strong Mathematical Background in analysis and linear algebra
- Other important elements: Strong Academic and Algorithmic Skills, Motivated and Eager to Solve Problems, Motivated to Establish a Solid Foundational Background.
Application
The application must include:
- Detailed curriculum,
- List of publications specifying the three most important publications,
- Motivation letter of two pages also presenting the perspectives of research and education,
- Name and address of three references.
Applications should be submitted by e-mail to secretariat@eurecom.fr with the reference : CS/DM/DistComp/032026
Start date: 06/2026
Type of employment contract : Fixed-term doctoral position in private law
