Postdoc, Timone Faculty of Medical and Paramedical Sciences in Marseille, France.| Entreprise/Organisme : | SESSTIM - Aix-Marseille University | | Niveau d'études : | Doctorat | | Date de début : | As soon as possible, depending on administrative recruitment deadlines | | Durée du contrat : | 12 months, with the possibility of extension | | Rémunération : | Postdoctoral level; Aix-Marseille University salary scale | | Secteur d'activité : | Machine learning; Computer science; Engineering; Mathematics; Medical science | | Description : | The candidate will work in the multidisciplinary "Quantitative Methods and Medical Information Processing (QuanTIM)" team, comprising researchers in epidemiology and public health, statisticians, biostatisticians, computer scientists and data scientists. More specifically, he/she will be assigned to a project involving the application and development of Artificial Intelligence techniques to data from cancer registries. The aim of the work will be to develop or adapt a machine learning methodology in order to estimate excess mortality in the case of insufficiently stratified general population life tables.
Activities:
As part of the MIRACLE project (Méthodologie et Intelligence aRtificielle pour lA recherche épidémiologique en CancéroLogiE sur bases de données), funded by the French Ligue contre le Cancer, the candidate will contribute, for the benefit of patients and to decision-making in public health, to the enhancement of cancer databases, particularly the population-based ones. In this context, a key indicator measured in the general population is net survival, which represents the survival that would be observed in a hypothetical world where the studied disease is the only cause of death. By considering mortality due to other causes, which is derived from general population life tables stratified by certain variables, it enables comparisons to be made between populations and trends. However, using insufficiently stratified general population life tables leads to biased estimates of excess mortality. Different approaches have been considered and different models have been proposed to estimate excess cancer mortality for variables not directly observed in general population life tables. However, the existing models are based on certain assumptions that may be considered too strong given the needs and epidemiological questions. The candidate will familiarize himself/herself with various existing approaches and models, and then investigate the contribution of machine learning-based approaches based. He/she will develop or adapt a methodology based on machine learning (k-means, random forests or others) to estimate excess mortality in the case of insufficiently stratified general population life tables. The methodology developed should be adaptable to the situation where the number of variables not directly observed in the general population life tables is not limited. The candidate will assess the performances of these different methods through simulation studies. He/she will place particular emphasis on the interpretation of the methods, focusing on the epidemiological interpretability of the results obtained. He/she will implement the whole in an R package, preferably, or in another language depending on what is most suitable for practical application. In collaboration with other project investigators, he/she will write the article(s) on this work for publication in international peer-reviewed journals whether methodological or applied. | | En savoir plus : | https://sesstim.univ-amu.fr/fr/offre-d-emploi
PostDoc_MIRACLE-MLRT.pdf | | Contact : | nathalie.graffeo@univ-amu.fr |
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