Post-doc position in Data Science and Statistical learning for quality control in medical device manufacturing
12-month fixed-term contract (2024- 2025)
Where: in the Saint-Etienne (42000) Campus of Mines saint-Etienne ( France)
The postdoctoral position is part of a collaboration between Thuasne in Saint-Étienne and Mines Saint-Etienne with the Institut Henri Fayol. Mines Saint-Etienne is on of the graduate scholls of Institut Mines Télécoms.
The aim of this project is to optimize quality control in the manufacture of medical devices by exploiting the various data available in the production chain.
The candidate should have a PhD in applied mathematics, or data science or computing sciences with a background in statistical learning. Experience in anomaly detection and causal analysis techniques will be particularly appreciated.
Expected skills include
- Data analysis and processing
- Data science, statistical learning
- Machine learning, deep learning and pattern identification
- Methods and algorithms for anomaly detection and cause identification techniques.
The Institut Henri Fayol, a training and research center at Mines Saint-Etienne, focuses on current transformations in the digital, ecological and industrial transitions that are at the heart of the efficiency, resilience and sustainability of industry and territories. It develops a multi-disciplinary strategy combining strong skills in mathematical and industrial engineering, computer science and intelligent systems, environmental and organizational engineering, and responsible management and innovation, in conjunction with the EVS UMR 5600, LIMOS UMR 6158 and COACTIS research units.The postdoctoral position is part of a collaboration between Thuasne in Saint-Étienne and Mines Saint-Etienne with the Institut Henri Fayol ( and UMR Limos).
Context : Technological developments linked to Industry 4.0 can be used to solve quality control issues (Quality Control 4.0). The digitalization and digitization of production chains has made a numerous data, accessible and usable for quality control. However, these data are not sufficiently exploited to ensure improved control and production.
The aim is to optimize quality control by detecting potential anomalies among the available data and information, and by analysing the root causes of production faults.
Keyds words : Quality Control, Statistical Learning, Machine Learning, Anomaly Detection, Root Cause Analysis, Medical Device ,Value Stream Mapping
Missions :
- Analysis of available data sets to identify data that could be used to analyse and control quality .
- Selection of the state-of-the art machine learning methods that best fit the data selected for the detection of non-quality or manufacturing defects.
- Development, testing and validation of the various quality control methods.
-Transfer of knowledge and results to the industrial partner
Applications must be submitted on the RECRUITEE platform by 14th May 2024 at the latest, via the following link (as soon as possible) :https://institutminestelecom.recruitee.com/o/post-doctorant-ou-post-doctorante-en-sciences-de-donnees-pour-le-controle-de-qualite-dans-la-fabrication-de-dispositifs-medicaux-cdd-12-mois-potentiellement-renouvelable-2
Contact: Pr Mireille Batton-Hubert EMSE/FAYOL : Mireille.BATTON-HUBERT@emse.fr
(c) GdR IASIS - CNRS - 2024.