Post-doctoral research position on plant disease identification based on deep learning

The Inria center at Université Côte d'Azur includes 42 research teams and 9 support services. The center’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 regional 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.

This position will be funded as part of the Pl@ntAgroEco project which goal is to design, test and develop new services for agroecology within the Pl@ntNet platform.  

The control of plant diseases is a major challenge to ensure global food security and sustainable agriculture. Recently, deep learning based image recognition techniques have shown very promising results towards improving existing procedures for early detection and diagnosis of plant diseases. However, the performances are still insufficient and needs to be significantly improved through (i) the integration of new massive training data at large taxonomic and geographic scales (in particular via ePhytia and Pl@ntNet), and (ii) the development of more effective AI models combining visual information (photos) with other environmental and contextual information (e.g. climate, land use, soil, etc.). The selection of these complementary modalities will be based on their benefit in terms of recognition accuracy but also in terms of their ease of integration and maintenance in the Pl@ntNet platform. 

Main activities :

• data cleaning and structuring
• training and evaluation of image classification models (from self-supervised foundation models)
• training and evaluation of multi-modal plant disease prediction models 
• technological transfer in collaboration with Pl@ntNet engineers

Additional activities :

• Writing of scientific papers
• Participation to project meetings
• presentation in conferences

Technical skills and level required :

• PhD in data science
• strong experience in deep learning 
• strong skills in python, pytorch

Other valued appreciated :

• experience in training large-scale deep learning models on super-computers
• knowledge in life sciences
• experience in collaborative work contexts 

Language : english

• 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 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
• Contribution to mutual insurance (subject to conditions)

Gross Salary : 2788 € per month