Cifre Phd-autonomous Reinforcement Learning
il y a 1 semaine
**Informations générales**:
**Entité légale **:Chez Framatome, filiale d'EDF, nous concevons et fournissons des équipements, des services, du combustible, et des systèmes de contrôle-commande pour les centrales nucléaires du monde entier.
Nos 18 000 collaborateurs permettent chaque jour à nos clients de produire un mix énergétique bas-carbone toujours plus propre, plus sûr et plus économique.
Nos équipes développent également des solutions pour les secteurs de la défense, de la médecine nucléaire et du spatial.
Implantée dans une vingtaine de pays, Framatome rassemble les expertises d'hommes et de femmes passionnés et convaincus que le nucléaire est une énergie d'avenir.
Entreprise responsable, nous développons des actions pour former et accompagner les premières expériences professionnelles (label Happy Trainees), intégrer tous les talents, dont les personnes en situation de handicap, œuvrer pour l'égalité professionnelle et la mixité de nos métiers (94/100 à l'index de l'égalité hommes-femmes) et concilier les temps de vie.
**Référence **:2024-18564
**Date de parution **:26/09/2024
**Description du poste**:
**Métier**:
TA - ETUDES - CONCEPTION & INGENIERIE - TAL - Contrôle commande et Instrumentation nucléaire
**Intitulé du poste**:
CIFRE PhD-Autonomous reinforcement learning quadrotor navigation for complex industrial environment F/H
**Contrat**:
CIFRE
**Fourchette de rémunération**:
de 35 à 40 k€
**Description de la BU**:
dti
**Description de la mission**:
This CIFRE PhD deals with robotics drone navigation with machine learning.
In recent years, the use of drones in various industrial sectors has seen significant growth due to their potential to improve efficiency, safety, and data collection capabilities. However, navigation in complex indoor environment such as factories, in an autonomous fashion raises many complex limitations, including localization, obstacle avoidance, and autonomous planning. Some of these limitations are being addressed in part by the academic sector, such as approaches for using YOLO for inspecting power lines and animals, or for avoiding complex and obstacles rich environments, while also out matching the state-of-the-art in drone control by human and optimal control systems.
This PhD is funded by CIFRE, with a joint supervision between Framatome and Central Supelec (SYCOMORE laboratory).
**Profil**:
- Strong background in robotics, computer science, machine learning, embedded systems, or a related field;
- Proficiency in programming languages such as Python or C/C++;
- Good analytical and problem-solving skills, with a strong passion for discovery and cutting-edge research;
- Effective communication skills in English, and the ability to work both independently and as part of a multidisciplinary team;
- Experience with robotics platforms, sensors, deep reinforcement learning, advanced mathematics, or the Robotic Operating System (ROS) would be ideal.
**Localisation du poste**:
**Localisation du poste**:
France, Ile-de-France, Hauts-de-Seine (92)
**Site**:
La Defense
**Durée du contrat en mois**:
36
**BU**:
DTI - DTIP
**Niveau d'études min. requis**:
Bac+5
**Niveau d'expérience min. requis**:
Jeune diplômé
**Niveau d'emploi**:
Cadre
**Informations additionnelles**:
**Poste soumis à enquête administrative**:
Oui
**Poste soumis à autorisation au titre du contrôle des exportations**:
Non
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