The Distributed Computing group at ETH Zurich is looking for a PhD candidate to work on the SNSF Ambizione 2023 project “eDIAMOND: Efficient Distributed Intelligent Applications in Mobile-Network Dynamics”. The eDIAMOND project aims at developing new methods and systems for decentralized and distributed data-driven methods for Federated Learning on resource-constrained networks. Your research within the project will contribute towards your doctoral degree at ETH Zurich. You will be supervised by Prof. Dr. Roger Wattenhofer and Dr. Antonio Di Maio.

You will be entrusted with designing, developing, and evaluating data-driven methods, algorithms, and systems for three independent but related research directions in the eDIAMOND project, namely:

1. Distributing model training and inference over a network of resource-constrained devices.
2. Online, context-aware adaptation of Federated Neural Network Architectures based on the available system resources (e.g., communication, computation, energy).
3. Communication-efficient knowledge exchange among networked federated large models.

These research directions allow you to gradually build your own research profile according to your interests, while remaining within the project’s goals. Each research direction is composed of a sequence of Tasks that will collectively achieve the project’s goal. For each Task, you will be responsible for the typical scientific research workflow: motivating the problem, identifying the main methodological shortcomings in the literature, design and develop novel systems, plan and execute experiments, and report findings in articles to be published at top venues according to the project’s schedule. Periodic meetings and feedback will ensure the success of your degree and of the project overall.

Other info

• The PhD Survival Guide from ETH Zurich
• All about the doctorate at ETH Zurich

We are looking for a new member with the following profile:

1. A strong interest and motivation to contribute to the eDIAMOND project’s goals (see Job description section above).
2. Theoretical and practical experience and passion in at least one of the following topics:

• Machine Learning: Deep Learning, Federated Learning (vertical, horizontal), Split Learning, Model Selection, Knowledge Distillation, Low-Rank Adaptation (LoRA), Large Models (Language, Vision).
• Decentralized and Distributed Systems: Gossip Protocols, Consensus Algorithms, (Byzantine) Fault Tolerance, Peer-to-Peer Networks.
• Online Algorithms: Partially-Observable Markov Decision Processes (POMDPs), Cooperative and Competitive Multi-Agent Reinforcement Learning (MARL), Multi-armed Bandits, Bayesian Optimization.
• Automated Model Design and Tuning: Neural Architecture Search, Hyperparameter Optimization.
• Computer Networking: Resource-Constrained Networking (e.g., Internet of Things), Wireless and Mobile Communications (e.g. IEEE 802.11, Bluetooth, etc.), Protocols (e.g., MAC, Network, and Transport).
• Mathematics: Statistical Learning, Stochastic Processes (e.g., Percolation Theory, Queuing Theory, Age of Information), Network Calculus, Graph Theory, Convex and Non-convex Optimization, Approximation Algorithms.

1. An excellent Master’s degree in Computer Science, Engineering, Mathematics, or other fields related to the project’s domain, from a reputable university.
2. A strong Transcript of Records, stating the list of passed exams (particularly those related to the project and the areas above) and their grades.
3. Strong programming skills (i.e., Python and main Machine Learning frameworks such as Pytorch).
4. Experience with scientific writing (e.g., reports, theses, and scientific articles).
5. Strong critical thinking, English communication skills (oral and written), interpersonal abilities, collaboration aptitude.

Bonus points:

• Published peer-reviewed articles on project-related topics in reputable conferences or journals
• Experience with network simulators such as OMNeT++ or ns-3
• Experience with High Performance Computing (i.e., SLURM)
• Experience with Git