Doctoral Student in Information and Coding Theory for Federated Learning
Develop advanced information and coding theory methods to enhance federated machine learning resilience and efficiency.
Best for:Best for graduate students with a strong mathematical background and an interest in information theory, coding theory, and federated learning.
Overview
This doctoral position focuses on information- and coding-theoretic methods to improve the resilience and efficiency of federated machine learning under limited bandwidth and unreliable nodes. Jointly supported by KTH Royal Institute of Technology and the Technical University of Denmark (DTU), the project requires significant mobility, including at least one year spent at DTU. The successful candidate will conduct independent research, collaborate across international research groups, and analyze complex stochastic systems. Supervision will be provided jointly by faculty members across both institutions.
What you'll do
- Conduct theoretical research on information- and coding-theoretic methods for federated learning
- Analyze and improve resilience and efficiency in distributed machine learning systems
- Collaborate closely with research teams at both KTH and the Technical University of Denmark (DTU)
- Fulfill mobility requirements by spending a cumulative minimum of one year conducting research at DTU
- Publish research findings in peer-reviewed academic venues and present at international conferences
- Participate in doctoral-level coursework and academic activities within the department
Requirements
Degree required: Master's degree or second-cycle degree equivalent to at least 240 higher education credits
Degree offered: PhD
Language: English equivalent to English B/6
Instruction: English
- Strong background in the theoretical analysis of stochastic phenomena
- Proficiency in the mathematical analysis of engineered systems
- Demonstrated ability to pursue independent research and tackle complex analytical problems
- Strong interpersonal and collaborative skills for international teamwork
- Passed a second cycle degree (e.g., master's degree) or completed at least 240 higher education credits with 60 second-cycle credits
Funding & terms
How to apply
Submit your application through the official university recruitment portal by following the online instructions and providing all required academic and personal documents.
- CV
- Master's degree transcripts
- Cover letter
Contact
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