PhD Position in Assessing and Explaining Deep Learning Model Compression
Explore innovative methods to evaluate and interpret deep learning model compression techniques for improved efficiency and transparency.
Best for:Best for computer science PhD candidates with a strong background in deep learning, machine learning, and a keen interest in model optimization and interpretability.
Overview
This PhD position at École Nationale Supérieure des Arts et Métiers (ENSAM) focuses on advancing research in deep learning model compression. The successful candidate will investigate novel approaches to assess and explain the performance, efficiency, and interpretability of compressed models. Under the supervision of experienced researchers, the PhD student will contribute to cutting-edge work in computer science, bridging theory and practical applications. The project aims to enhance the understanding of model compression while ensuring robustness and transparency in AI systems. Applicants should be passionate about AI research and eager to tackle challenges in model optimization.
What you'll do
- Develop and evaluate novel methods for assessing deep learning model compression techniques.
- Investigate the impact of compression on model performance, efficiency, and interpretability.
- Conduct experimental studies to validate theoretical approaches and propose improvements.
- Collaborate with research teams to integrate findings into broader AI research projects.
- Present research findings at academic conferences and publish results in peer-reviewed journals.
- Contribute to the development of tools or frameworks for model compression analysis.
Requirements
Degree required: Master's degree (or equivalent) in Computer Science, Artificial Intelligence, or a related field
Degree offered: PhD
Language: English proficiency; French proficiency is not explicitly required but may be advantageous
Instruction: English
- Strong background in deep learning and machine learning principles
- Proficiency in programming languages such as Python
- Experience with machine learning frameworks (e.g., TensorFlow, PyTorch)
- Familiarity with model optimization techniques and neural network architectures
- Basic knowledge of statistical analysis and experimental design
- No specific nationality restrictions
- No residency requirements mentioned
- No age limits specified
- No prior experience caps mentioned
Funding & terms
How to apply
Deadline: 2026-10-22
Applicants should submit their application through the official Campus France portal for doctoral positions. The application process typically requires a CV, a cover letter, a research proposal, academic transcripts, and letters of recommendation. Ensure all documents are in English or accompanied by certified translations if necessary.
- CV
- Cover letter
- Research proposal or statement of purpose
- Academic transcripts (BSc and MSc)
- Letters of recommendation (typically 2-3)
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