2022-04742 - Research engineer in computer vision
Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Contrat renouvelable : Oui

Niveau de diplôme exigé : Bac + 5 ou équivalent

Fonction : Ingénieur scientifique contractuel

A propos du centre ou de la direction fonctionnelle

The Inria Grenoble - Rhône-Alpes research center groups together almost 600 people in 22 research teams and 7 research support departments.

Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE, …), but also with key economic players in the area.

Inria Grenoble - Rhône-Alpes is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.



Contexte et atouts du poste

The recruited candidate will start working at Inria, in accordance with the latest health advisories in France. The position is compatible with a partial work-from-home scenario.


Is regular travel foreseen for this post ?

In view of the global pandemic situation, the candidate is not expected to travel internationally for work. International travel may only be necessary to present the results of the work once the restrictions are lifted. In such a case, work-related travel expenses will be covered.



The research engineer will work at the Inria Rhone-Alpes research center near Grenoble. The unit includes more than 600 people, within 26 research teams and 10 support services. Grenoble is a lively city which hosts many foreign students and researchers. Located in the heart of the French Alps its direct surroundings offer great outdoor recreation including skiing, cycling, and hiking. Grenoble is well-connected to Lyon and Geneva airports and Paris can be reached from Grenoble in 3h by train.

Mission confiée

The scientific objective of this project is to find effective alternatives for supervised learning in the context of image classification. This is motivated by the fact that annotating data with labels is a time-consuming and error-prone process. Furthermore, the data itself may have missing samples, or worse contain errors. One potential solution in this context is the use of unsupervised clustering techniques, which divide the dataset into groups of related samples based on a similarity measure. Although this research topic is being actively explored in the research community [1, 2], several open questions remain: what is an appropriate feature space for this clustering? can it be learnt together with the similarity measure? should all samples be treated equally? In collaboration with ANTAI, we will target such research questions in the context of a large-scale, in-house image dataset composed of cars, along with more traditional public benchmarks.
[1] Ji et al., Invariant Information Clustering for Unsupervised Image Classification and Segmentation, ICCV 2019.

Principales activités

  • Implement state-of-the-art deep clustering algorithms
  • Develop new algorithms and evaluate them on standard benchmarks and the in-house datasets
  • Write research papers for top-tier conferences and/or journals
  • Present the research results to peers
  • Participate in research activities in the team (e.g., seminars, reading groups)
  • Participate in maintaining the team's infrastructure


The candidate should have a good expertise in Python and deep learning frameworks such as PyTorch. She/He should also have a good background in computer science, understand mathematics, and be interested in computer vision and machine learning.


We welcome candidates:

• with a master or PhD degree, and who are interested in the engineering and practical aspects of research,

• or who are young engineers who have recently graduated from an engineering school, and who would like to discover a lively academic research environment (before possibly pursuing later a career in research),

• or who have a previous experience as research engineer.


  • 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 (sick children, moving home, etc.)
  • Possibility of teleworking (90 days / year) and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage under conditions


From 2,562 € (depending on experience and qualifications).