*Project *
Social learning strategies are a key component of human intelligence and of
our ability of learning from and collaborating with other humans in their
environment. Inspired by this, some initial research efforts are enabling
embodied AI agents with social and cooperative skills, thus permitting them
to coordinate and collaborate with and to learn from other agents and
humans. The goal of this Ph.D. thesis is to devise innovative cooperative
learning algorithms to support the navigation of embodied AI agents in
dynamic complex environments populated by other agents and humans and to
learn how to interact and communicate with other heterogeneous agents to
learn collaboratively world models. The ideal candidate would have a
research interest in cooperative and embodied AI, multi-agent deep
reinforcement learning, neuro-symbolic approaches, etc. The candidate will
have the possibility of working within the ELLIS network and in
collaboration with top international and national universities.

*Benefits:*
*Fully funded* 3-year position + other benefits (e.g., lunches at the
internal canteen are covered, internal cafeteria with reduced prices,
flexible working hours and others)

*Location:*
Trento, Italy - Bruno Kessler Foundation
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*Apply at *
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*Additional Information:*
Please reach out to talk about the position. Email le...@fbk.eu (Dr. Bruno
Lepri 
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and cc seraf...@fbk.eu (Dr. Luciano Serafini
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--
*Massimiliano Luca*
*Ph.D. Candidate - **Faculty of Computer Science, Free University of
Bolzano*
*Ph.D. Candidate - MobS Lab, Fondazione Bruno Kessler*

*Previously: MIT, Telefonica Research, IFISC (Spanish National
Research Council), Italian National Research Council, Stellantis - Centro
Ricerche Fiat*

E-mail: ml...@fbk.eu
Twitter: 
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