13/10/2020

Research Scientist on Human Action Recognition


  • ORGANISATION/COMPANY
    MINES ParisTech - Centre for Robotics
  • RESEARCH FIELD
    Computer scienceOther
    MathematicsProbability theory
  • RESEARCHER PROFILE
    Recognised Researcher (R2)
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    13/11/2020 21:00 - Europe/Brussels
  • LOCATION
    France › Paris
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35
  • OFFER STARTING DATE
    02/11/2020

OFFER DESCRIPTION

Introduction:

The Centre for Robotics of MINES ParisTech, PSL Université Paris, is involved in several research projects on human motion pattern recognition applied to the Factory of the Future, the Creative and Cultural Industries and the Autonomous Vehicles. The main objective of these projects is the development of novel methodologies and technological paradigms that improve the perception of the machine and allows for natural body interactions in human-machine partnerships.

Topic:

MINES ParisTech is opening a short-term position for a research scientist on 'Development of an end-to-end embedding of a CNN into a HMM', which is horizontal on various H2020 and industrial projects. The most recent advances of Convolutional Neural Networks  (CNNs) in computer vision, have also shown promising results in human action recognition. Nevertheless, in most previous CNN-based approaches the stochasticity of the human movement, which can also be seen as a temporal evolution of video data, is not properly taken into account. The majority of the studies make use of a simple sliding window while evaluate the output as a per-frame overlap with the ground truth. Furthermore, very often CNNs are trained on a frame-level while only a very few datasets provide frame labels. In practice, this is very rarely the case, especially for real-time human action recognition in professional environments or other real-life data scenarios. Stochastic models, such as Hidden Markov Models (HMMs), manage well tasks where the inputs have a variable length. The objective of this short-term recent position is to model the emission probability of the HMM by an embedded CNN, which has more powerful image modelling capabilities than generative models such as Gaussian Mixture Models, in a bayesian framework.

The candidate will have to: 1. perform the appropriate training of the CNN (using also transfer learning when needed), 2. propose a method to convert the posterior probability of the CNN to class-conditional likelihoods, 3. add a number of hyper-parameters to mesure the effect of both the CNN structure and the states of the HMMs to the hybrid approach, 4. deliver an implementation of the approach for professional action recognition, which will be tested in various use-cases, such as the LCD TV assembly, the riveting of aircrafts parts, etc. and 5. compare the results with the so-called "tandem approach" where the CNN is not used as a classifier but to extract features that are then modelled by a GMM.

This position will give the possibility to the candidate to work with other European researchers both in the project and in the wider academic community, as well as opportunities to work with industrial partners. Finally, the candidate will be autonomous and concentrated on his/her work while some assistance in the related teaching duties of the Post-Master’s Degree AIMove is also expected.

More Information

Benefits

The candidate will have a short-term contract between 6 and 10 months according to his/her studies and level of experience. The gross monthly salary will depend on the profile/experience of the candidate.

Eligibility criteria

Completed five years of studies and have received a Master or a PhD.

Selection process

Please send your CV and a motivation letter to the address: sotiris.manitsaris@mines-paristech.fr

Additional comments

Offer Requirements

  • REQUIRED LANGUAGES
    ENGLISH: Excellent

Skills/Qualifications

We are looking for a motivated and talented young researcher (Postdoc or Engineer) who has completed at least five years of studies or has received a Master Degree, or a Phd, in one of the following domains:

  • Machine and Deep Learning
  • Computer Vision
  • Pattern Recognition

Specific Requirements

The candidate researcher should have excellent skills on:

  • Machine and/or Deep Learning
  • Signal Processing
  • Parallel programming in GPUs: C++, Python, R.

Map Information

Job Work Location Personal Assistance locations
Work location(s)
1 position(s) available at
MINES ParisTech - Centre for Robotics
France
Paris
75006
60, Boulevard Saint-Michel

EURAXESS offer ID: 567248

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