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Active1mo ago

PhD position H/F - Assessing and Explaining Deep Learning Model Compression

Cesi·📍 Vandoeuvre les nancy, Grand Est, France

Employment

FULL TIME

Work mode

On-site

Experience

MID LEVEL

Salary

Not disclosed

Salary not disclosed

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Role overview

Job description

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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About this listing

  • Source: Cesi's official careers site (via Recruitee) · last confirmed on the source 30 Oct 2026
  • Posted 21 Sept 2026

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What they are looking for

Skills & requirements

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

Department · Recherche

Hiring company

Cesi

Education

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