The Ubiquitous Internet Unit of IIT-CNR is seeking talented researchers to explore Causal AI in pervasive systems.
Job Description
We are collecting expressions of interest for a postdoctoral research position with the specific research direction adaptable to the candidate's expertise.
Candidate Profile
* MSc or PhD in Computer Science, Mathematics
* Proficiency in programming (e.g., Python, RL frameworks)
* Expertise in AI/Machine Learning
Research Topic
Traditional machine learning and deep learning approaches primarily focus on correlation-based learning, identifying statistical associations between variables. However, to enable more robust, explainable, and human-centric AI, the next step is to shift from learning mere correlations to discovering causal relationships.
Research Objectives
1. Establish a synergy between heterogeneous electronic devices—including smartphones, wearables, IoT devices, and virtual assistants—and causal explainable AI.
2. Design and deploy decentralized, human-centric causal learning frameworks that can set up and analyze causal experiments in real-world settings.
Target Applications
Pervasive systems will be the primary focus, exploring how causal intelligence can enhance decision-making, adaptive learning, and autonomous system behavior.
Key Research Areas
* Theoretical modeling of causal inference in AI.
* Algorithm and system design for deploying causal learning on pervasive devices.
* Exploration of Causal Reinforcement Learning to improve adaptive decision-making in uncertain environments.
Funding and Partnerships
The activities of this topic will be supported by FAIR: Extended Partnership on Artificial Intelligence, funded by the National Recovery and Resilience Plan (NRRP), European Union - NextGenerationEU.