Annonce en lien avec l’Action/le Réseau : aucun
Laboratoire/Entreprise : LRI, Université Paris-Sud
Durée : 1 year
Contact : silviu.maniu@lri.fr
Date limite de publication : 2017-11-20
Contexte :
The Internet of Things (IoT), the large network of physical devices that extends beyond the typical computer networks, will be creating a huge quantity of Big Data streams in real time in the near future. The realization of IoT depends on being able to gain the insights hidden in the vast and growing seas of data available. Since current approaches do not scale to Internet of Things (IoT) volumes, new systems with novel mining techniques are necessary due to the
velocity, but also variety, and variability, of such data. This challenging setting needs algorithms that use an extremely small amount (iota) of time and memory resources, and that are able to adapt to changes while not stopping the learning process. These algorithms should be distributed to allow them to run on
top of Big Data infrastructures. How to do this accurately in a fully automatic, and transparent elastic, real-time, system is going to be the main challenge for IoT analytics systems in the near future. In this scenario, high-performing ensemble setups such as online bagging, leveraging bagging and random forests
are the state-of-the-art. Deep neural networks are becoming increasingly popular, owing in part to the proliferation of interest and oft-advertised successes in deep learning. These algorithms can learn incrementally, but they have so far proved too sensitive to hyper-parameter options and initial conditions to be considered for the IoT data stream setting.
Sujet :
We are looking for candidates for a postdoc, with funding
available for one year, starting the latest on January 1st, 2018, in the area of Stream Data Mining with application to IoT scenarios.
Profil du candidat :
The successful candidate should have solid bases in data management, algorithms and knowledge of machine learning topics, and have proven research experience in the fields of data mining or machine learning. There is an important application development part in this project. As such, the applicants should be at ease with
programming in well-known languages (C++, Java, Python).
Formation et compétences requises :
– PhD degree in Computer Science
– proven research record in the field of data mining, with stream data mining a plus
– easy with programming in Java especially, with C++ or Python a plus
Adresse d’emploi :
The successful candidate will join the LRI lab of Université Paris-Sud,
part of Université Paris-Saclay. They will integrate the LaHDAK team whose research orbits around Web data management. The work will be performed in close collaboration with with the DIG team in LTCI lab of Télécom ParisTech.