Postdoc in Real-Time Distributed Learning & Privacy

1000scholars; Villeurbanne; On-site; EUR 30,000 - 42,000; Full time

Job summary

Inria seeks a postdoctoral fellow to advance real-time, distributed learning at the edge within the PRIDeL project, in collaboration with Simula and NYCU.

The position is for 12 to 24 months with a start date around November 1, 2026 and not later than January 1, 2027. Candidates must have defended their PhD no later than December 31, 2026, and mobility between France and partner countries is encouraged.

Qualifications

• The successful candidate should have completed a PhD on either theoretical or experimental aspects of either:
• Distributed or federated learning
• Learning with time series data
• Differential privacy
• Communication networks for learning

Responsibilities

• Collaborate with Egan, Yin, and Huang on learning, communications and privacy in distributed learning.
• Develop distributed learning algorithms for streaming data.
• Analyze convergence and generalization in real-time distributed systems.
• Integrate algorithms into a holistic framework for edge inference networks.

Skills

Distributed learning

Federated learning

Time-series learning

Differential privacy

Communication networks

Education

PhD in a relevant field

Tools

Grid5000

Job description

Inria seeks a postdoctoral fellow to advance real-time, distributed learning at the edge within the PRIDeL project, in collaboration with Simula and NYCU.

The position is for 12 to 24 months with a start date around November 1, 2026 and not later than January 1, 2027. Candidates must have defended their PhD no later than December 31, 2026, and mobility between France and partner countries is encouraged.