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Rennes, Bretagne, France INRIA Temps pleinJob Description:The Inria Rennes - Bretagne Atlantique Centre is seeking a highly motivated PhD researcher to work on a project focused on continual learning for edge devices. The successful candidate will be part of the TARAN team and contribute to the development of novel techniques for training and deploying continual learning systems on edge devices.Key...
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Rennes, Bretagne, France INRIA Temps pleinJob Description:The Inria Rennes - Bretagne Atlantique Centre is seeking a highly motivated PhD researcher to work on a project focused on continual learning and low-precision arithmetic for edge AI applications. The successful candidate will be part of the TARAN team and contribute to the development of novel techniques for training and deploying continual...
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Rennes, Bretagne, France INRIA Temps pleinJob DescriptionPhD Position in Continual Learning for Edge DevicesInria Rennes - Bretagne Atlantique Centre is seeking a highly motivated PhD candidate to work on a research project focused on continual learning for edge devices. The project aims to investigate the performance impact of using low-precision arithmetic in the context of training and deploying...
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Rennes, Bretagne, France INRIA Temps pleinPhD Position in Continual LearningThe Inria Rennes - Bretagne Atlantique Centre is a major player in the field of digital sciences, with a rich R&D and innovation ecosystem. We are seeking a PhD researcher to work on a project that aims to investigate the performance impact of using low-precision arithmetic in the context of training and deploying continual...
Researcher in Incremental Machine Learning for Edge AI
Il y a 2 mois
Context and Objectives
This PhD position is part of the Adapting project, which focuses on designing adaptive embedded hardware architectures for AI. The goal is to develop new incremental machine learning algorithms that can serve as use cases in the Adapting project for other researchers who will focus on the hardware architecture design.
Key Challenges
- Designing resource-efficient incremental learning algorithms that can run on embedded systems with limited computational power, memory, and energy efficiency.
- Developing algorithms that can handle real-time processing with low latency and deterministic behavior.
- Ensuring efficient updates while handling data locally to enhance privacy and security.
Research Objectives
- To investigate incremental learning of foundation models on embedded systems.
- To explore how state-of-the-art methods can be applied to these models, assessing the extent to which these existing techniques provide effective solutions to the problem of incremental learning.
- To develop and propose new methodologies specifically designed to enable foundation models to learn incrementally when deployed on embedded systems, ensuring that these models maintain performance and adaptability over time.
Requirements
- A master's degree in computer science, AI, or a closely related discipline.
- A strong background in computer science and mathematics.
- A scientific attitude and the ability to reason through problems.
- Excellent programming skills.
- The ability to communicate written and orally in English in a clear and precise manner.
- A pro-active and independent attitude as well as the ability to function well in a team environment.
Benefits
- Subsidized meals.
- Partial reimbursement of public transport cost.
Salary
A monthly gross salary amounting to 2100 euros.