CENTRALE LYON - Post-Doctoral Position Ferroelectric-Based Ternary Computing: From Circuit Design to System-Level Integration Scientific Context
CENTRALE LYON€30.9K – €36.6K/yr
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Role overview
Research Work Scientific Context The success of deep neural networks is often constrained by their reliance on large amounts of labeled data, which are both costly and time-consuming to obtain. Prior work has demonstrated that models can be compressed by up to 84% without any loss in performance [4]. At the same time, self-supervised learning (SSL) has emerged as a promising alternative, enabling models to learn from unlabeled data. Moreover, SSL models offer the advantage of being adaptable to multiple downstream tasks thereby reducing the cost associated with training models for auxiliary tasks. In this context, we have proposed novel approaches for SSL model compression [15], particularly in the domains of speech recognition and emotion detection, achieving results that surpass the current state of the art. However, the increasing complexity of deep neural networks raises significant
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Qualification
The PhD thesis addresses these challenges by investigating the combined potential of Green AI approaches and explainable AI (XAI) techniques; Organisation It is a joint French-German PhD thesis that will be registered in ENSAM (France) and in THM (Germany); Double PhD degree will be delivered conditioned on a successful PhD thesis; PhD co-supervisor
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