PhD position in Causal AI
Pubblicato il 11-08-2026 - IIT-CNR in Toscana
Fully funded 3-year Ph.D. position in Causal AI within the National PhD AI Program at the University of Pisa (Italy) is sponsored by the Ubiquitous Internet (UI) research group of IIT-CNR . We are looking for a highly motivated Ph.D. candidate with a strong academic background to join our research team and work on this Ph.D. topic under our supervision.
The call is now open! This is a scouting notice to raise awareness: the University of Pisa has officially opened Ph.D. admissions, and applications are now being accepted.
Deadline: 10 August 2026, 13:00 CE
Program start: 1 November 20
Duration: 3 yea
Research To
Traditional machine learning approaches primarily focus on correlation-based learning, identifying statistical associations between variables. Shifting from correlations to causal relationships is one of the most promising directions toward AI that is more robust, interpretable, and useful in practi
ce.This research explores how heterogeneous devices (smartphones, wearables, IoT sensors, and edge systems) can be used as a substrate for causal learning in the wild. The idea is to leverage data naturally collected from distributed, device-rich environments (smartphones, wearables, and IoT sensors) as a substrate for causal learning frameworks that can extract causal knowledge from observational data in real-world setting
Research Focus & Methodo
logyThe target applications focus on pervasive systems, where the heterogeneity and scale of device-generated data create both unique challenges and opportunities for causal reasoning. In these settings, causal knowledge can play a key role in improving decision-making and adaptive behavior — for instance, enabling systems to generalize across different devices and environments, act robustly under uncertainty, or understand the consequences of their actions rather than merely reacting to observed patte
rns.Depending on the background and interests of the candidate, research activities may incl
- ude:- Theoretical modeling of causal inference in (distributed) AI setti
- ngs.- Algorithm and system design for deploying causal learning on pervasive devi
- ces.- Using causal representations to improve decision-making, planning, or generalization under uncertai
- nty.- Experimental evaluation through simulations and real-world deployme
Ideal Candidate Pro
- file:MSc in Computer Science, Mathematics, Physics, or a related
- fieldStrong foundation in probability, statistics, and machine lea
- rningBackground or interest in causal inference, sequential decision-making, or pervasive sy
- stemsComfortable with Python and relevant frame
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