Post-Doctoral Research Visit F/M Levels Merging in the Latent Class Model

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Type de contrat : Fixed-term contract

Niveau de diplôme exigé : PhD or equivalent

Fonction : Post-Doctoral Research Visit

A propos du centre ou de la direction fonctionnelle

Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 15 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region.

For over 10 years, the Inria center at the University of Lille has been at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT).

Contexte et atouts du poste

This post-doc position will hold within the framework of a partnership of 2 Inria teams specialized in statistics and machine learning: MODAL/DATAVERS in Lille and CELESTE in Saclay.

The hosted research team will be MODAL/DATAVERS in Lille. Some travels are planed between the two teams for several specific working groups.

The new DATAVERS Inria team is a very recent spin-off of the MODAL team, involving now a strong partnership with a multidisciplinary research team in Public Health. By this way, the methological part of this post-doc activity will be experimentally validated on real data sets coming directly from the medical context.

Mission confiée

The research topic concerns the latent class model (LCM), dedicated to cluster categorical variables, when the number of levels is large, situation frequently encountered in practice. A recent work proposes to extent LCM to a natural modeling which limits the number of levels by merging them, process which is also equivalent to a specific levels clustering.

A description of this seminal work, including also state of the art, bibliography and scientific references are available at the following URL, pages 719-724 :

https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Docenti/Università/bozza-book-compresso-new1.pdf

The main research tasks will be twofold: (1) developping a strategy for efficiently explore the combinatorial space of merging levels and (2) to apply this strategy on real medical data sets suffering from a large number of levels.

Principales activités

Main activities:

  • Innovative methodology for efficient merging levels
  • Model implementation (R or Python) though a dedicated package
  • Numerical evaluation of the model on medical data
  • Publication in a statistical or a machine learning international journal

Compétences

Technical skills and level required : solid skills in mixture models and related estimation algorithms

Languages : solid skills in R and/or Python

Relational skills : excellent interpersonal skills

Avantages

  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
  • Possibility of teleworking and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage

Rémunération

2 788 € monthly gross salary