Starting research position in deep learning for ecological modeling

April 16, 2023
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2023-05873 - Starting research position in deep learning for ecological modeling

Contract type : Fixed-term contract

Level of qualifications required : PhD or equivalent

Fonction : Tempary Research Position

Level of experience : Recently graduated


The Pl@ntNet team is looking for a post-doc with strong skills in machine learning (ideally in deep learning and Bayesian models), ecology and Python development. Pl@ntNet is a citizen observatory of plant biodiversity and a research platform at the crossroads of data science, ecology and artificial intelligence. The Pl@ntNet mobile application is used by tens of millions of users worldwide.

You will work mainly in the Montpellier Computer Science Laboratory (LIRMM) as a starting researcher at Inria. Inria is a major player in computer science research worldwide; it is the managing organization of the Pl@ntNet consortium. You will work in a quite unique environment at the forefront of digital technologies for biodiversity management. In particular, you will work in the context of the B3 EU project aimed at mobilizing biodiversity data cubes in conjunction with other environmental data and scenarios, as the basis for models and indicators of past, current and future biodiversity.

You will interact regularly with the two senior researchers at Inria (Alexis Joly and Diego Marcos) and periodically with the ecologists, engineers and other researchers involved in Pl@ntNet and B3.


The main assignment will be to develop innovative AI models for species distribution modeling at continental scale. The objective is to demonstrate the use of data cubes with recently developed deep learning models as a way to monitor biodiversity more efficiently.

Two scientific challenges wil be adressed in particular:

(i) taking into account observation bias: species observations stemming from citizen science platforms are increasingly leveraged to gather information about the geographic distributions of many species. However, their usability is limited by the strong biases inherent to these community-driven efforts. These biases in the sampling effort are often treated as noise that has to be compensated for. In this project, we posit that better modelling the sampling effort (including the usage of the different platforms across countries, local accessibility, attractiveness of the location for platform users, affinity of different user groups for different species, etc.) is the key towards improving Species Distribution Models (SDM) using observations from citizen science platforms, thus opening up the possibility of leveraging them to monitor changes in species distributions and population densities.

(ii) taking into account long-term spatio-temporal dependencies: experiments with convolutional neural networks (CNN) have shown that they have the ability to capture complex information about the spatial structure of the environment and landscape, and that this information allows for better prediction of observed species occurrences. One limitation of CNNs, however, is that their architecture induces an intrinsic inductive bias in the sense that they process the signal only locally (e.g. in the neighborhood of each pixel), and thus, they cannot accurately model long-term or long-distance relationships. They are therefore not really suitable for predicting future trajectories or for predicting the impact of local changes on more distant areas (e.g. to model invasion risks). Transformers, on the other side, go beyond local processing and exploit extremely long-term dependencies for increased performance, but their use in geographic information systems is still a largely unexplored field.

Main activities

Main activities:

  • design of AI models mixing deep learning (transformers) and Bayesian Belief Networks
  • structuring of large-scale multi-modal training data sets
  • experimentation of the designed models (on super-computer)
  • results dissemnitation (scientific publication(s), models/data sharing, conferences, etc.)
  • participation to the general meetings of the B3 EU project
  • Skills
  • Technical skills and level required:

  • Very good knowledge in deep learning and statistical learning
  • Good knowledge in ecology
  • Experience in Python development, GitHub
  • Languages : English
  • Relational skills: Autonomy, intellectual curiosity, ability to work collaboratively and interdisciplinarily.
  • Benefits package
  • 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
  • Social security coverage
  • Remuneration

    From 3039 € gross monthly (according to degree and experience)

    General Information
  • Theme/Domain : Earth, Environmental and Energy Sciences
  • Town/city : Montpellier
  • Inria Center : Centre Inria d'Université Côte d'Azur
  • Starting date : 2023-06-01
  • Duration of contract : 1 year, 6 months
  • Deadline to apply : 2023-04-16
  • Contacts
  • Inria Team : ZENITH
  • Recruiter : Joly Alexis / [email protected]
  • About Inria

    Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.

    Instruction to apply

    Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.

    Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities.

    Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed.

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