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Title: Improving food security systems by linking heterogeneous data – The case of agricultural production in West Africa
This thesis aims at the improvement of Food Security Monitoring systems through the use of heterogeneous data, focusing on the management of agricultural production risks. While agroclimatic data (e.g., satellite imagery, climate information, etc.) has been widely used for this task, the use of data coming from different domains (i.e., household surveys, social media, press, business analyses) has often been neglected. Remote sensing data is widely used for real time monitoring of vegetative growth, but is not sufficient to explain complex food safety-risk phenomena. The aim of this thesis is twofold: (i) to define innovative data mining techniques that will be able to exploit this heterogeneous data context. To reach this goal, three phases have been identified: (a) automatic discovery of spatial features from heterogeneous data, (b) features linking (i.e., through the definition of new similarity measures between features) and (c) data mining (i.e., through the definition of new network analysis, clustering and deep learning techniques) ; (ii) to show how remote sensing data can be enriched by linking it to data from different domains in order to make it more suitable for food safety-risk analysis tasks. During this thesis, we will focus on studies carried out in Burkina Faso, by exploiting satellite (with vegetation and climate features), economic, and textual data. The analytical framework will be based on retrospective analysis, focusing on the crop failures of 2007 and 2011 in Burkina Faso as major cases of studies. We will possibly extend our study to other areas, using data collected in Senegal. Given the interdisciplinary path at the basis of this work, the results of the analysis and the defined techniques are expected to generate significant interest in socio-economic, remote sensing, and data mining fields. During the PhD period, the student will also participate in short term missions (e.g., periods of two or three weeks) to West Africa, working with experts in the field of remote sensing and food security. This PhD is co-funded by Cirad (https://www.cirad.fr/en) and by the Convergence Institute "Digital Agriculture" #DigitAg (http://www.hdigitag.fr/en).
The PhD student will be hosted in the TETIS Laboratory in Montpellier (France). TETIS lab is a Joint Research Unit (JRU) among IRSTEA, CIRAD, AgroParisTech and CNRS. The TETIS JRU conducts methodological research concerning the management of spatial information. It uses an integrated approach of the spatial information chain, beginning with its acquisition (especially by Earth observation systems) and including its processing, management and use by stakeholders.
The ideal candidate will have:
A strong background in computer science (data mining, machine learning, image analysis).
Background knowledge in the field of remote sensing will be a plus.
Interest and/or an experience in applied sciences, particularly in the agronomy/environment/geography domains, will be welcomed.
He or she should have completed, or about to complete, a MSc.
Good programming skills in languages such as python, Java and C++ will be a plus.
Good written and spoken English.
A two page CV.
A one page motivation letter explaining why their skills, knowledge and experience make them a particularly suitable candidate for the given position.
The last academic transcripts of their studies.
The contact details of one or two referees; do not send reference letters.
The application deadline is June 20, 2018