Internship Research
Il y a 5 mois
Le descriptif de l’offre ci-dessous est en Anglais_
**Type de contrat **:Convention de stage**Niveau de diplôme exigé **:Bac + 3 ou équivalent**Fonction **:Stagiaire de la recherche**Niveau d'expérience souhaité **:Jeune diplôméA propos du centre ou de la direction fonctionnelle
The Inria centre at Université Côte d'Azur includes 37 research teams and 8 support services. The centre's staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM...), but also with the regiona economic players.
With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.
Contexte et atouts du poste
Mission confiée
This rich and extensively annotated dataset will be shared with the researchers from the Joint Research Unit TETIS, enabling the intern to conduct several studies to assess and quantify the capabilities of neural network-based semantic segmentation methods to detect fine-grained habitat mapping on the available very high resolution (airborne) imagery.
Principales activités
The research internship will have the following activities:
- Conduct a literature review on Semantic Segmentation methodologies for habitat mapping from very high spatial resolution imagery;
- Explore and perform analytics on the ECO-MED dataset;
- Perform initial Semantic Segmentation tests on the data provided by ECO-MED with state of the art approaches (CNN / ViT);
- Adapt and customize state of the art Semantic Segmentation approaches to deal with the ECO-MED dataset. Possible research paths that will be explored are: i) hierarchical semantic segmentation or ii) semantic segmentation with sparse data;
- Quantitative and qualitative evaluation of the proposed method compared to state-of-the-art competitors;
- Preparation of the internship report.
Compétences
- Python programming
- Deep Learning with Python (preferably with Pytorch)
- Experience with Remote Sensing imagery
Avantages
- 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 (after 6 months of employment) 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
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