Postdoctoral Researcher in Resource-Efficient Machine Learning
il y a 3 jours
Context and Key Features of the Position
Within the framework of a partnership between Inria and the Ecole Normale Supérieure de Lyon, we are seeking a postdoctoral researcher to contribute to the development of resource-efficient machine learning systems.
Founded in 1880, the Ecole Normale Supérieure de Lyon is one of the most prestigious Grand Écoles in France, with a strong focus on training future professors, researchers, and leaders in various disciplines.
Inria, established in 1967, is the only public research body in France dedicated to computational sciences, with a mission to invent the digital technologies of the future.
The recruited postdoc will conduct a vigorous research program within the scope of the project, focusing on the development of machine learning systems with guaranteed utility, resource-efficiency, and trustworthiness.
The successful candidate is expected to bring expertise in sparse deep networks and statistical machine learning, and will be encouraged to develop collaborations with other groups at ENS de Lyon.
The position is endowed with travel, computing, and experimental resources, and offers a gross salary of 2788 € per month.
Main Activities
The postdoc will be responsible for conducting research in the following areas:
- Expressivity and Robustness of Sparse Deep Networks
- Provable Algorithms for Sparse Deep Learning
- Random Sketches for Efficient Manifold & Graph-based Learning
Required Skills
Candidates should hold a Ph.D. in a relevant field, and have a strong background in applied mathematics and statistics, as well as good programming skills.
Previous experience in sparse signal representations or statistical machine learning is preferred, but experience in related areas is also suitable.
Benefits
- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave
- Possibility of teleworking (90 days / year) and flexible organization of working hours
- Access to vocational training
- Social security coverage under conditions
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