PhD Positions in the context of i-Nondations

When:
31/12/2017 – 01/01/2018 all-day
2017-12-31T01:00:00+01:00
2018-01-01T01:00:00+01:00

Annonce en lien avec l’Action/le Réseau : aucun

Laboratoire/Entreprise : IRIT
Durée : 3 ans
Contact : patricia.stolf@irit.fr
Date limite de publication : 2017-12-31

Contexte :
Two PhD student positions are proposed. Both PhD thesis will be done in the context of SEPIA team specialised in large scale distributed systems (HPC and Clouds), particularly in autonomic computing, scheduling and multi-objective optimization. SEPIA team is part of IRIT lab (750 staff members) in Toulouse.

These PhD thesis will be done in the context of the i-Nondations (e-flooding) project funded by ANR (French National Funding Agency). This project is a collaboration with Cerema, IRSTEA, Enedis and SDIS31.
Every year floods happen. Solutions exist for slow floods but fast ones are difficult to predict and to handle. This project aims to model fast floods in term of risk management and impact on the infrastructures using data collected by technological or human sensors.
The project will integrate different technical expertise to handle fast floods in crisis management and resilience.
The project aims to integrate different expertises in an autonomic approach for a fluid adaptation to the evolutions and to the events.

The project suggests managing three phases: before, during, and after a crisis in a feedback loop coming from the autonomic field called MAPE-K loop [1][2]. It is based on four steps : Monitoring, Analysis, Planning and Execution with a Knowledge database. The Knowledge database will be filled continuously in order to identify similarity between events, study answers (and optimize answers) and construct different solutions to handle crisis.

Two loops will be used : one for short term timescale and one for long term.
The short term one will aim to handle the crisis while the long term one will aim to prevent other crisis. Both loops will interact through a learning process.

In this context, two PhD subjects are proposed:

Sujet :
The first one is entitled “Autonomous and optimized flood management” and aims to study how to use the MAPE-K approach in crisis management and particularly floodings in order to optimize the management.

This PhD will follow the following three steps:
1) State of the art
-state of the state on formal descriptions for crisis management and for simular use cases
-state of the art on MAPE-K use in crisis management
-state of the art on optimization techniques

2) Expected research contributions:
-to model heterogeneous data coming from technological sensors (or human sensors), from the environment, from previous events, from decisions made during previous crisis …
-to propose different algorithms to deal with a flooding situation with different objectives
-to evaluate and compare the performances of different algorithms in a given context.
-to study the accuracy of the algorithms depending of the current context.

3) Implementation and validation:
Integration in a simulation framework to simulate different crisis situations and to evaluate the algorithms on different scenarios.

The second one is entitled “e-flooding machine learning” and aims to study machine learning to make the interaction between both autonomic loops. The aim of this process will be to detect similar circumstances between different events/situations.

This PhD will follow the following three steps:
1) State of the art
-state of the state on formal descriptions for crisis management /for simular use cases
-state of the art on machine learning techniques
-state of the art on large scale event based situations (for example Complex Event Processing)

2) Expected research contributions:
-to propose different machine learning algorithms to be able to learn from the context and the past evolution
-to evaluate and compare the performances of different algorithms in a given context.
-to study the accuracy of the algorithms depending of the current context.

3) Implementation and validation:
Integration in a simulation framework to simulate different crisis situations and to evaluate the learning algorithms on different scenarios.

Profil du candidat :
Requirements:
– A Master in Computer Science
– crisis management skills will be bonus

Formation et compétences requises :
– Programming skills (for example Python, Java)
– Fluent in English, French is bonus

Adresse d’emploi :
IRIT,
Université Toulouse 3 Paul Sabatier
Toulouse

Document attaché :