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Post-doctoral Position: Federated Learning for
Il y a 5 mois
ENSAM is a traditional engineering/research graduate school (Grande École), recognized for leading French higher education in the fields of mechanics and industrialization. Founded in 1780, it is among the oldest French institutions and is one of the most prestigious engineering schools in France. The school is a "Public Scientific, Cultural and Professional Institution" which has trained more than 85.000 engineers since its foundation.- The Mechanical, Surface, Materials, and Processes (MSMP) Laboratory is at the ENSAM campus located in Aix-en-Provence, France. The city is located in the region of Provence-Alpes-Côte d'Azur, close to the Mediterranean Sea in the south of France, a fact that gives it a warm climate with an average of 300 days of sunshine per year.- Under the scope of the Horizon European project MARS:
- **M**anufacturing
- **A**rchitecture for
- **R**esilience and
- **S**ustainability, the MSMP Laboratory hosted by the**Site web**:
**Poste et missions**:
Context: The European manufacturing industry is majorly composed of SMEs, and they could take advantage of digitalization to join the world’s competitive market. As economic, sanitary, and societal crises take place more often than usual, supply chain disruptions are experienced creating a shortage of products in many sectors of the economy. The MARS project aims to enable SMEs to access advanced innovations in the field of AI-driven digital manufacturing processes and enter into process chains that are geographically distributed, making the manufacturing industry more resilient to crises. For that, the MARS project will create a Distributed Fractal Manufacturing System (DFMS), which is composed of manufacturing platforms that are interconnected. The DFMS will work as a coordinator hub connecting different manufacturing platforms (nodes). Each node of the DFMS will work as a local convergent manufacturing platform with the following databased decision-making/support tools: i) CAPP (Computer Aided Process Planning), ii) multi-agent distribution and scheduling of manufacturing tasks, iii) digital and data-hashed (blockchain) certification of measurements, iv) Proactive Quality Control (PQC). Each manufacturing platform will have its own local data management and will share only AI models and quality data with the system through the use of Federated Learning.
As a result, the project aims to introduce radical flexibility into manufacturing by redefining the process route, raw material, resources, technology, throughput, manufacturing site, delivery date, proven product quality, and sustainability.
**Job description**:
Your task concerns mainly item iii), in which you will work together with project partners (Ubitech, Arxum, Noosware) to develop the architecture and framework of the DFMS platform.
The platform must be able to:
- Provide different levels of production-related information (procurement & planning, execution & supply chain, quality tracking)
- Perform machine/process data acquisition in relative real time;
- HMI for operator inputs and process monitoring;
- Exchange of AI models via Federated Learning with the DFMS cloud system.
The DFMS platform must also be capable of accommodating the other tools (i, ii, and iv) that will be developed by other R&D teams.
Post-doctoral duration: 2 years
**Mobilité géographique**:
- Internationale**Télétravail**:
- Occasionnel**Prise de fonction**:
- 03/04/2023**Profil**:
**Your profile**:
Equal opportunities
**Objectifs**:
Your task concerns mainly item iii), in which you will work together with project partners (Ubitech, Arxum, Noosware) to develop the architecture and framework of the DFMS platform.
