On-going offersImpact of Quality of Working life on Company Performances ?
A post-doc position is opened at the crossroad of Data Science and Humanities; the goal is to investigate the relationships between quality of working life (based on nation-wide polls) and company performances (based on data from the Ministry of Industry). The position is funded by
- Profile: The successful candidate holds a PhD in Machine Learning or Econometrics, Management or Sociology of Labor. Good programming skills are required.
- When: from June 2016
- Duration: 1 year
- Contact: Philippe Caillou (caillou at lri dot fr) and Michele Sebag (sebag at lri dot fr)
Please send a CV, a statement of research interest, a link to the PhD dissertation and available publications, and a list of three references.
Un post-doc est ouvert en Apprentissage Statistique pour les Sciences Humaines et Sociales, portant sur l'analyse des relations entre qualité de la vie au travail et performance des entreprises. Les données (disponibles) sont issues des Ministères du Travail et du Budget. Le candidat.e aura un doctorat en apprentissage machine ou en économétrie, gestion quantitative ou sociologie du travail. Le CDD sera localisé sur le campus de l'université Paris-Saclay.
Large-scale optimization: new algorithms and benchmarking
- Detailed description is available here: https://www.lri.fr/~auger/PostDocEngineerProposal.pdf
- When: from July 2015
- Contact: Anne Auger (INRIA TAO)
Bio-inspired algorithms for intrusion detection
- Official post is here
- Description of work: The postdoctoral fellow will be in charge of a survey of the recent advances in the field of intrusion detection from network streams (not limited to bio-inspired methods). She/He will select the most promising avenues, supervise their implementation by a team of engineers from the industrial partner, conduct detailed comparisons among them, and ultimately propose original approaches to address their weaknesses and tackle their limitations.
- Profile: The successful candidate should hold a recent PhD in Computer Science, with a strong expertise in bio-inspired algorithms and/or data stream mining. Knowledge of cyber-security is more than welcome, but not mandatory. On the other hand, programming skills are absolutely necessary, and software engineering experience is welcome, too.
- When: from Oct. 1. 2015
- Duration: 2 years
- Contact: Marc Schoenauer (INRIA TAO)
Please send a complete CV, a statement of research interest, a link to the PhD dissertation and available publications, as well a list of three references.
Autonomic auto-tuning for Machine Learning
- contact Cecile Germain (Universite Paris Sud)
- more information: http://www.lri.fr/~cecile/JOBS/PostDocAutoTuning.pdf
- general goal: a fully automatic system that can adapt the configuration parameters of parallel machine learning algorithms and their hyperparameters on the fly
Machine Learning and Optimization for Long Term Investment Planning (around March 2013)
- contact Olivier Teytaud (INRIA-LRI)
- More info: http://www.lri.fr/~teytaud/metis.html
- Optimization; Energy; Machine Learning; MDP solving
Machine Intelligence for Manufacturing and Design (Sept. 2011)
Application of Machine Learning methods to Search and Optimization (Sept. 2011)
- Funded by the Microsoft-INRIA joint Lab., contact Youssef Hamadi (Microsoft Research Cambridge) and Marc Schoenauer (INRIA Saclay)
- more details
Collaborative development in planing
Bandits or Monte-Carlo Tree Search
Traffic modelling and inference (was Jan. 2009)
- Funded by ANR project Travesti, located in Armines (Paris)
- ANR Travesti Details
Deep Networks (was March 2010)
Evolutionary Planning (was Jan. 2010)
Automatic Parameter Tuning (was Sept. 2009)
- Open position in the Adaptive Search team at Microsoft Research - INRIA joint lab.
- Contact: Marc Schoenauer
Symbolic Learning in Swarm Robotics (was April 2009)
Position at Kyushu University (Japan) funded by Japan Science Technology Agency
Post-Doc Kyushu 2009
Learning in collective robotics (was October 2008)
Supervised Machine Learning for EEG (was March 2008)
Automatic tuning of search and optimization algorithms: (July 2007).
See details here
Machine Learning for Evolutionary Robotics:
(May 1. 2007). Funded by INRIA - Details and application
Automatic Evolutionary Generation of Test Data:
Magneto-Encephalography Data Mining for Brain Computer Interface:
Evolutionary generation of mesh topologies from positive examples only.