Mines Saint-Etienne (MSE), one of the graduate schools of Institut Mines Télécom, the #1 group of graduate schools of engineering and management in France under the supervision of the Ministry of the Economy, Industry and Digital Technology, is assigned missions of education, research and innovation, transfer to industry and scientific, technological and industrial culture.
MSE consists of 2,400 graduate and postgraduate students, 400 staff, a consolidated budget of €46M, three sites on the Saint-Etienne campus (Auvergne Rhone-Alpes region, Lyon Saint-Etienne metropolitan area), a campus in Gardanne (SUD region, Aix Marseille metropolitan area), a site in Lyon within the digital campus of Auvergne Rhone-Alpes Region, six research units, five teaching and research centres and one of the leading French science community centres (La Rotonde €1M budget and +40,000 visitors per year). The Times Higher Education World University Ranking ranked us for 2022 in the 251-300 range for Engineering and Technology. Our work environment is characterised by high Faculty-to-Student, Staff-to-Faculty and PhD-to-Faculty ratios, as well as comprehensive state-of-the-art experimental and computational facilities for research, teaching and transfer to industry. Website: https://www.mines-stetienne.fr/
Common accidents of everyday life represent the third cause of death worldwide. Fall is the most frequent accident of everyday life and the first cause of accidental death for those aged 65 years and over. As such, accidental falls and the fear of falling represent an obstacle to healthy and active aging that must be urgently addressed.
The risk of fall has been reported to increase with age due to many interconnected factors (cognitive, physical and environmental). In this context, project PREVICHUTE proposes a multi-faceted approach to anticipate and prevent everyday falls based on surveillance, evaluation and physical training. Most of the existing tools that predict accidental fall use data reflecting the physical condition of an individual (e.g., data from medical sensors or wearables). In contrast, our approach will primarily use alternative data types, which have thus far been seldom explored for fall prediction, such as data from insurance claims and patient surveillance. The value of adding data from clinical trials and communicating devices will also be assessed in the project. Ultimately, project PREVICHUTE will generate and make use of the most diverse dataset for fall prediction to date.
PREVICHUTE is a consortium-based project conducted by partners that combine the following expertise:
Calyxis (profiling victims and circumstances of accidents, epidemiology and clinical experimentation)
Tecnalia (digital architectures and connected biomechanical devices)
Mines Saint-Etienne (data mining, machine-learning algorithm development)
AQUI O Thermes (tracking fragile patients and conducting thermal-based treatments)
Domitys and Clinique Marienia (clinical evaluation in senior care and rehabilitation facilities)
The main deliverable of project PREVICHUTE is a digital platform to quantify the risk of fall of an individual who may or may not have had previous accidental falls. The successful candidate will be responsible for developing machine-learning algorithms to (i) integrate the diverse datasets used in the project; (ii) automatically determine the most predictive features associated with the risk of fall; (iii) generate a score that reflects the risk of fall for an individual; (iv) estimate the quality of the predicted score. She/he will be expected to closely collaborate with other partners in the consortium.
Importantly, she/he will be expected to generate scientific publications.
2/ Profile of the candidate
PhD degree in data science, computer or industrial engineering, signal/image analysis or related fields; solid knowledge of machine-learning theory; experience in machine-learning algorithm development; good programming skills; capacity to manage projects and to successfully integrate a research team; interest in partnership-based research (direct industrial partnerships, collaborative research); writing skills with significant scientific production; knowledge of French and English. Previous experience in machine-learning applications in healthcare is a bonus.
3/ Recruitment conditions
Duration: 18 months, full time
Desired start date: May 1st, 2023
Remuneration according to the rules defined by the Institut Mines Télécom's management framework
Place : Saint-Etienne (42)
3/ Application procedures
Apply by March, 27th, 2023 on the RECRUITEE platform:
Candidates selected for an interview will be informed rapidly. Part of the interview will be held in English.
4/ For more information
Research contact :
Administrative contact :
Data protection :
École des Mines de Saint-Étienne is France’s oldest elite engineering school outside Paris, and the fifth oldest among France’s 250 « Grandes École...Visit the employer page