Postdoctoral Researcher Pisa

Postdoctoral Researcher
Pisa
Toscana, Pisa

The Ubiquitous Internet Unit of IIT-CNR (Pisa, Italy) is scouting for talented researchers (post-doc level).

We are collecting expressions of interest for the following research topic:

Causal AI in pervasive systemsThe position is open at postdoctoral level, with the specific research direction adaptable to the candidate's expertise.

Applicants should have a strong foundation in AI/ML, causal inference.

Candidate profile MSc or PhD in Computer Science, MathematicsProficiency in programming (e. g. , Python, RL frameworks)Expertise in AI/Machine LearningOn the 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.

This research aims to establish a synergy between heterogeneous electronic devices—including smartphones, wearables, IoT devices, and virtual assistants—and causal explainable AI.

By leveraging our hyperconnected environments, we seek to design and deploy decentralized, human-centric causal learning frameworks that can set up and analyze causal experiments in real-world settings.

The target applications will focus on pervasive systems, exploring how causal intelligence can enhance decision-making, adaptive learning, and autonomous system behavior.

One key area of interest is Causal Reinforcement Learning (CRL), which extends standard reinforcement learning by enabling agents to understand cause-and-effect relationships rather than just learning from rewards.

CRL can significantly improve decision-making under uncertainty, leading to AI systems that are more robust, sample-efficient, and interpretable, which is crucial for pervasive AI applications operating in dynamic real-world environments.

Depending on the expertise and interests of the candidate, research activities may include:

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. Performance evaluation through experiments, large-scale simulations, and real-world analysis. 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).

Further information: *

Seniority levelEntry level

Employment typeFull-time

Job functionResearch, Science, and Information Technology

IndustriesResearch Services

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