Context : The overall objective of our work is to model and control (self-)adaptive systems. More specifically, we consider systems composed of a multitude of components, small in size and similar in nature, that collaborate for a common global goal, as for example in the context of the Internet-of-Things (IoT). Modular robots (https://projects.femto-st.fr/programmable-matter/) are an example of such systems [1,2,8]. The structure of such systems is heterogeneous. Our modeling of such systems and the programming of elements/blocks must be based on measurements depending on structures/patterns and power points, in order to allow subsequent optimization of physical aspects such as, for example, energy consumption, and computer aspects such as, for example, memory space and number of messages exchanged.

Applicant profile : The proposed subject links theory (models: automata and finite state machines) and practice in the application domain (modular robots). It requires an interest in instrumentation of programmable robotic systems. Some knowledge of their programming will be appreciated.

Application deadline : before May 19th 2023

Contact: 

 PhD Supervisor : Olga Kouchnarenko - olga.kouchnarenko@femto-st.fr

Co-PhD supervisor : Frédéric Lassabe - frederic.lassabe@femto-st.fr

+ d'infos :

kouchnarenkolassabe_sujet_contrat_doctoral_2023.pdf

Job Description : The Applied Mechanics Department of the FEMTO ST Institute, has been involved in the hydrogen storage theme for 20 years, on issues related to mechanics and materials science. The recruitment aims at reinforcing the human resources for the HYperStock project. The HYperStock project is one of the 7 targeted projects of the PEPR - Decarbonized Hydrogen (France 2030) which aims to consolidate the French scientific leadership in the field of storage and distribution of hydrogen under high pressure. Reducing the carbon impact of compressed hydrogen transport and storage solutions requires action on the materials used, by integrating the way they are obtained, the transformation processes, and their recyclability. This project aims to establish a "materials in a severe H2 environment" reference system, coupled with selection methodologies, and is divided into two main families of materials

• Non-metallic materials (elastomers, thermosets, thermoplastics and composites)
• Metallic materials (steels, aluminum alloys...

Profile : The candidate must have a Master's degree in mechanics or materials science and a proven taste for experimentation as well as knowledge of modeling. Specific skills in polymeric materials will be appreciated. Rigor, autonomy, teamwork and good communication skills (written and oral) in French and English are expected from the candidate. A strong sensitivity to sustainable development and life cycle analysis will be particularly appreciated and valued.

Contact: 

frederic.thiebaud@univ-fcomte.fr

david.chapelle@univ-fcomte.fr

 anne.maynadier@univ-fcomte.fr

+ d'infos :

sujetthese-equipemateco-2023.pdf

Job context and motivation : Most of the real physical system and everyday situations include uncertainty. This is the case for medical diagnosis, weather forecasting, evolution of the stock market and so on. In the literature two types of uncertainty are distinguished: aleatoric uncertainty denotes the one that is inherent to the data, e.g., noise in measurements or natural variability of the inputs, and epistemic uncertainty related to the model and due to lack of knowledge. Measuring the uncertainty is important, so as to support the user in the action to take. For example, when an anomaly is detected, with weak confidence level, another source of information should be added (image, human intervention, etc.) before planning intervention actions. More generally, quantification of the prediction uncertainty allows to trust or not predictions. In fact, incorrect overconfident predictions can be harmful and lead to erroneous decision.

Applicant profile: Master in applied mathematics (or equivalent). Probability, statistic. Good skills in Python programming. Experience in machine learning/deep learning
Financing Institution: MESRI
Application deadline: 15 June 2023
Start of contract : Fall 2023

How to apply: Please send a motivation letter, a detailed CV and transcript of results to the above email addresses

Contact: 

Noura Dridi (noura.dridi@ens2m.fr),

Zeina Al Masry (zeina.al.masry@ens2m.fr),

Noureddine Zerhouni (zer- houni@ens2m.fr)

+ d'infos :

thesisofferfemto.pdf