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Post-Docs Offers

On-going offers
Causality for Program Synthesis
A 2-years post-doc position is opened at TAU, as part of the INRIA Project Lab HyAIAI (Hybrid Approaches for Interpretable Artificial Intelligence - https://project.inria.fr/hyaiai/). The position will be split between the TAU team at INRIA Saclay (base location) and the LACODAM team at INRIA Rennes (frequent visits from Saclay, all expenses covered of course). The work will tackle learning causality from data, as a path to better explainable models, building on TAU expertise using Deep Generative models. LACODAM will provide the use-case in progam synthesis: real-time recommendation for computer usage, learned from logs of user activity.
  • Details: available here
  • Profile: The successful candidate holds a PhD in Machine Learning. Good programming skills are required, in Python with Scikit Learn and TensorFlow? or PyTorch?,
  • When: from Dec. 2019
  • Duration: 2 years
  • Contact: Marc Schoenauer (Marc dot Schoenauer at inria 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.

Past offers
Impact 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
University Paris-Saclay.
  • 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


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

Machine Learning and Optimization for Long Term Investment Planning (around March 2013)

Machine Intelligence for Manufacturing and Design (Sept. 2011)

Application of Machine Learning methods to Search and Optimization (Sept. 2011)

Collaborative development in planing

Bandits or Monte-Carlo Tree Search

Traffic modelling and inference (was Jan. 2009)

Deep Networks (was March 2010)

Evolutionary Planning (was Jan. 2010)

Automatic Parameter Tuning (was Sept. 2009)

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)
Details

Supervised Machine Learning for EEG (was March 2008)
Details

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:

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Magneto-Encephalography Data Mining for Brain Computer Interface:

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Evolutionary generation of mesh topologies from positive examples only.

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Contributors to this page: evomarc , sebag , auger , cecile , Olivier and furtlehn .
Page last modified on Tuesday 22 of October, 2019 09:01:50 CEST by evomarc.