A 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.

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.If the topic sounds like something you'd enjoy working on, please send us your CV and academic transcripts combined into a single PDF (LinkedIn doesn't allow multiple file uploads) to get preliminary feedback on how your profile aligns with the topics funded by IIT-CNR in this call. Please note that, due to the number of applications expected, we'll be able to follow up only with candidates who are a strong fit. You're also welcome (and encouraged) to submit your application through the official call using the link below

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FZ?️ ????????: 10 August 2026, 13:00 CE

ST? ??????? ?????: 1 November 20

26⏳ ????????: 3 yea

**rs

Research To**

picTraditional 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

**s .

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

**nts.

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

**works

Who Should**

Apply?This is a PhD position. The specific research direction adaptable to the candidate’s expe rtise.

📌 PhD position in Causal AI (Toscana)
🏢 IIT-CNR
📍 Toscana