The PIRoS (Perception, Interaction and Robotique Sociales) team of the Institute for Intelligent Systems and Robotics (ISIR) at Sorbonne University (Paris) is looking for a for a highly motivated and ambitious postdoctoral researcher to conduct research on interactive machine learning and human-robot interaction for education.
The successful candidate will work on several EU projects including the HumanE-AI-Net (ICT-48-2020 – Towards a vibrant European network of AI excellence centres) and the Marie Skłodowska-Curie Innovative Training Network ANIMATAS (www.animatas.eu).
The HumanE AI Net brings together top European research centers, universities and key industrial champions into a network of centers of excellence that goes beyond a narrow definition of AI and combines world-leading AI competence with key players in related areas such as HCI, cognitive science, social sciences and complexity science.
ANIMATAS aims to develop intuitive human-machine interaction models and methods for education in schools. The project, coordinated by Sorbonne University, already recruited 15 ESRs (PhD students) working on three main research topics: (i) embodiment, (ii) social learning and (iii) personalized adaptation.
PIRoS is now opening a post-doc position with a focus on social learning with the aim of developing new machine learning models able to learn from multimodal human teaching signals. The successful candidate will develop new machine learning models grounded in cognitive science and education.
She/He will work in collaboration with the ANIMATAS PhD students and partners as well as potential collaboration with HumanE AI Network.
The successful candidate will ahem the opportunity to participate to the organization of training events (summer / winter schools, virtual events).
This position is for one year (12 months) contract, but there is a possibility to be extended depending on the performance and circumstances.
The ideal candidate must have a PhD degree and a strong background in machine learning, social robotics or cognitive science/neuroscience. The successful candidate should have:
- Good knowledge of Machine Learning Techniques
- Good knowledge of experimental design and statistics
- Excellent publication record
- Strong skills in Python
- Willing to work in multi-disciplinary and international teams
- Good communication skills
Interested candidates should submit their application before the end of September 2020, by sending the following documents in a single PDF file in an email addressed to firstname.lastname@example.org, with the subject: “Application Post-Doc Interactive ML”
- Curriculum vitae with 2 references (recommendation letters are also welcome)
- One-page summary of research background and interests
- At least three papers (either published, accepted for publication, or in-preparation) demonstrating expertise in one or more of the areas mentioned above
- Doctoral dissertation abstract and the expected date of graduation (for those who are currently pursuing a Ph.D)