진학프로석·박사 채용 접수 플랫폼

Context
The agricultural sector faces numerous challenges. Simultaneously, the development of digital technologies enables the generation of large volumes of spatialized data, which present a unique opportunity to better monitor crops and implement more efficient,sustainable, and also much more technical agricultural practices. Farmers alone will not cope with the scale and complexity of these transformations. More than ever, they will need personalized support from agricultural technical advisors, whose tools and methods must evolve.

UMR ITAP, specifically the DéMo team (Decision and Modeling for Agriculture), and Fruition Sciences (a viticulture consulting company) are partnering within a LabCom (ANR funding).Their joint research aims at developing tools to deploy a 3.0 version of agricultural advisory services: data-augmented advisory. This involves integrating data acquired from the many and diverse sources of available spatial information, analyzing these shared datasets to improve and standardize global expertise, and integrating the operational characteristics of farms to produce locally relevant advice.

We are recruiting a post-doctoral researcher who will participate in the initial work launched by the LabCom starting October 1, 2025 for 18 months.

Job description

State of the art
Agricultural data, derived from the technical and economic traceability of farms, are necessarily less structured than data acquired in a controlled experimental context: they are uncertain, heterogeneous, asynchronous, incomplete, sparse, etc. Various studies have shown the potential of this data to generate new knowledge adapted to the local context(Lamour et al., 2021; Laurent et al., 2022). However, these studies also highlighted the challenges related to the valorization of this data, such as the detection of local or global outliers (Leroux et al., 2018), spatial and temporal interpolation to address data incompleteness (Velez et al., 2022), and uncertainty management (Laurent et al., 2022).

Scientific objectives
Data analysis approaches are constantly evolving in data science but are relatively underapplied to agricultural data. The first objective of the post-doctoral position is therefore to conduct a state-of-the-art review of different data analysis approaches in agriculture,highlighting their specificities in relation to the data. This review can be built from the example of the databases provided by the DéMo team and Fruition Sciences. The aim is to create a typology of the data to be cross-referenced: multivariate, spatialized (homotopic or heterotopic), temporal (synchronous or asynchronous), for what purpose (exploratory,estimation, etc.), and to compare the associated analysis or processing methods, or even to propose a new approach if no method is identified.

By leveraging this new framework, the second scientific objective of the post-doctoral position is to implement and evaluate the selected method(s) on example databases, starting with the Fruition Sciences database.

Operational objective
The objective of the post-doctoral position is to develop a method to standardize (merge) an agricultural database, similar to the Fruition Sciences database, to serve two applications: i)an expert and ad hoc data analysis using a data visualization application, and ii) the implementation of statistical models for the estimation of agronomic parameters.

During the last semester, the post-doctoral researcher will work in collaboration with a computer developer from Fruition Sciences to enable the implementation of the standardization method in the company's platform and, if time allows, the data visualization application.

Work environment and conditions
The post-doctoral researcher will benefit from co-supervision by researchers from UMR ITAP and researchers from UMR MISTEA, in close relationship with Fruition Sciences, which will provide its database and expertise.
The post-doctoral researcher will be hosted at Fruition Sciences premises for part of the week and at the DéMo team premises for the other part of the week, both locations being in Montpellier, France.

References
Lamour, J., Le Moguédec, G., Naud, O., Lechaudel, M., Taylor, J., Tisseyre, B., 2021.
valuating the drivers of banana flowering cycle duration using a stochastic model and on farm production data. Precision Agric 22, 873–896.
https://doi.org/10.1007/s11119-020-09762-y

Laurent, C., Le Moguédec, G., Taylor, J., Scholasch, T., Tisseyre, B., Metay, A., 2022.
ocal influence of climate on grapevine: an analytical process involving a functional and Bayesian exploration of farm data time series synchronised with an eGDD thermal index: This article is published in cooperation with Terclim 2022 (XIVth International Terroir Congress and 2nd ClimWine Symposium), 3-8 July 2022,Bordeaux, France. OENO One 56, 301–317. https://doi.org/10.20870/oeno-one.2022.56.2.5443

Leroux, Corentin, Jones, H., Clenet, A., Dreux, B., Becu, M., Tisseyre, B., 2018. A general method to filter out defective spatial observations from yield mapping datasets. Precision Agric 19, 789–808. https://doi.org/10.1007/s11119-017-9555-0 Vélez, S., Rançon, F., Barajas, E., Brunel, G., Rubio, J.A., Tisseyre, B., 2022. Potential of functional analysis applied to Sentinel-2 time-series to assess relevant agronomic parameters at the within-field level in viticulture. Computers and Electronics in Agriculture 194, 106726. https://doi.org/10.1016/j.compag.2022.106726