Subject: Analysis of sensor network data flows

The monitoring and management of water resources is a major societal challenge in the context of global warming. One of the keys to informed decision-making is the availability of reliable data on resource levels and supply dynamics, as well as on consumption patterns.
To achieve this, it is essential to have a network of sensors enabling continuous monitoring of resources and their evolution as a function of meteorology and usage.

The first objective of the post-doctorate is to analyze the data collected on a network of sensors with a view to assessing the possibility of setting up diagnostic and alarm-triggering tools for resource managers.
In particular, the relevance of several Artificial Intelligence approaches will be explored on a platform developed at LIMOS.

In a second phase, the platform will be used to study other data flows from instrumented agroecosystems as part of the multidisciplinary collaborations of the ConnecSens Initiative .

Activities:
The work will consist of:
- Reviewing the state of the art in multivariate data flow analysis techniques;
- Become familiar with the SEDAF data flow anomaly detection tool developed at LIMOS;
- Propose solutions for improving SEDAF on project data flows.
- Develop the solution(s) chosen for the project
- Draw up a user and maintenance guide for the implemented solution
- Write technical reports on work carried out, as well as meeting minutes
- Organize and take part in work meetings
- Participate in the supervision of trainees within the framework of the project.

Applications are invited from PhDs in computer science or applied mathematics
or other disciplines with a strong background in data analysis and an appetite for
for computer science.

Duration: 18 months
Net monthly salary: 2430 euros
Start date: As soon as possible, preferably before the beginning of October 2024.
Responsibilities: Vincent Breton (LPC, IN2P3) et Engelbert Mephu Nguifo (LIMOS, INS2I)

More informations below