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Reference : UMR6625-SANRIG0-001
Workplace : RENNES
Date of publication : Thursday, September 30, 2021
Type of Contract : FTC Scientist
Contract Period : 12 months
Expected date of employment : 15 November 2021
Proportion of work : Full time
Remuneration : Around 2650 euros per month
Desired level of education : PhD
Experience required : 1 to 4 years
This post-doctoral project is part of the MODULO project which is dedicated to the statistical modeling of physical behavior by multidimensional modeling of accelerometric sequences in patients with symptomatic Peripheral Artery Disease (PAD). PAD is a serious chronic disease characterized by the narrowing or occlusion of one or more arteries in the lower limbs, which causes poor blood supply to the tissues. In 50 to 70% of cases, this pathology manifests as a limiting pain, appearing on walking, which forces the patient to stop to recover. PAD patients therefore have low levels of physical activity which is associated with a higher risk of mortality and a greater functional decline. Also, if the different therapeutic approaches in PAD have as primary objectives to reduce symptoms when walking and increase walking ability, it is therefore essential that the levels of physical activity of PAD patients also improve. The overall objective of the MODULO project and more particularly of this post-doctoral project is to address the issue of optimizing walking ability characterization by statistical modeling of the physical behavior of PAD patients based on accelerometric data collected from activity monitors.
These accelerometric data are characterized by a set of measurements collected at short and regular time steps and over a period of several days. Through a fine assessment of movement over several days, accelerometric data has the potential to describe and summarize physical behavior in a meaningful way. However, in the context of the study of PAD patients for whom the physical activity is very fragmented, one of the main challenges consists in extracting information from the activity sequences and their sequence to summarize in a multidimensional way the complexity of the patients' behavior. A second challenge is to establish relationships between the components of patients' physical behavior and their ability to walk. Indeed, characterizing physical activity as a series of sequences requires the development of innovative statistical models to take maximum advantage of the multidimensional nature of physical behavior. Most of the accelerometric data have already been collected during an on-going clinical trial.
The post-doctoral project is divided into three main axes:
- Axis #1: Characterizing sequences of activities to identify the principal components of the physical behavior by implementing an appropriate methodology.
- Axis #2: Modeling walking capacity from series of physical activity sequences.
- Axis #3: Evaluating the impact of a therapy on the association between physical activity and walking ability.
We look for highly motivated candidates with a PhD degree in applied statistics. Candidates must have strong knowledge of statistical methods in data analysis and good programming skills in R. Knowledge and skills in signal analysis and processing will be valued. Candidates must have aptitude to work in multidisciplinary projects with dynamism and autonomy.
This post-doctoral project will take place in the statistical unit of the IRMAR Lab.
The board of the MODULO projet is composed of complementary partners with skills in statistical modeling (Institut de Recherche Mathématique de Rennes – UMR CNRS 6625), in vascular medicine (CHU de Rennes) and in Sports science and exercise physiology (laboratoire Mouvement, Sport, Santé - M2S). Members of the project are:
• Mathieu Emily (firstname.lastname@example.org), Associate Professor at Institut Agro/Agrocampus Ouest and member of the Institut de Recherche Mathématique de Rennes (IRMAR – UMR CNRS 6625);
• Laurent Rouvière, Associate Professor at Université de Rennes 2 and member of the Institut de Recherche Mathématique de Rennes (IRMAR – UMR CNRS 6625);
• Alexis Le Faucheur, Associate Professor at the Ecole normale supérieure de Rennes and member of the M2S;
• Guillaume Mahé, Professor at the university hospital of Rennes and member of the M2S.
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