Print

Print


*****  To join INSNA, visit http://www.insna.org  *****

*Title: Improving food security systems  by linking heterogeneous data –
The case of agricultural production in West AfricaThis 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://urldefense.proofpoint.com/v2/url?u=https-3A__www.cirad.fr_en&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2x2A9TVGoSNfnWh9C4GuTctWHXaWieW2U3VCVzKEHX8&s=7D5C4-3QuBEkwUhLXD6pm3lu_AWjrvA4nFXf5durS1Y&e=
<https://urldefense.proofpoint.com/v2/url?u=https-3A__www.cirad.fr_en&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2x2A9TVGoSNfnWh9C4GuTctWHXaWieW2U3VCVzKEHX8&s=7D5C4-3QuBEkwUhLXD6pm3lu_AWjrvA4nFXf5durS1Y&e=>) and by the Convergence Institute "Digital
Agriculture" #DigitAg (https://urldefense.proofpoint.com/v2/url?u=http-3A__www.hdigitag.fr_en&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2x2A9TVGoSNfnWh9C4GuTctWHXaWieW2U3VCVzKEHX8&s=JFzPy0iZR-QqC8rUaUtPSPRQdxj3-IE7FkSncBzMdoc&e=
<https://urldefense.proofpoint.com/v2/url?u=http-3A__www.hdigitag.fr_en&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=2x2A9TVGoSNfnWh9C4GuTctWHXaWieW2U3VCVzKEHX8&s=JFzPy0iZR-QqC8rUaUtPSPRQdxj3-IE7FkSncBzMdoc&e=>). Hosting Laboratory: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.Candidates should send the following documents to
[log in to unmask] <[log in to unmask]>: - 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*

_____________________________________________________________________
SOCNET is a service of INSNA, the professional association for social
network researchers (http://www.insna.org). To unsubscribe, send
an email message to [log in to unmask] containing the line
UNSUBSCRIBE SOCNET in the body of the message.