PhD student positions

Find here a list of current and past PhD positions at SmartData@PoliTO lab.

For further information, we invite you to check the Politecnico PhD program here. You may also write us through the Contacts page.

Open positions in the 2025 spring session

For applications, please contact: contact@smartdata.polito.it.

Applications shall include:

  • The position you are interested into 
  • A statement of interest (half a page)
  • Curriculum Vitae (CV) 
  • Master’s degree transcript 
  • Eventual academic references 

All position are fully funded.

Deadline for the Application: November 14th, 2025 (check the official page for further details). Beginning of PhD: March, 2026.

Artificial Intelligence for Phishing Detection and Prevention

Supervisors:
Marco Mellia (DAUIN)
Matteo Boffa (DAUIN)

Description: Phishing is one of the most widespread and damaging cyberattacks, exploiting social engineering to deceive users via email, websites, or messaging platforms. Existing defenses, often based on static detection, struggle to keep pace with attackers’ rapidly evolving techniques.  

This research addresses the challenge in two phases. 

  • Phase 1: Observation and Analysis. The candidate will collect and analyze real-world phishing data, including malicious websites, phishing messages, and social engineering strategies. Data will be gathered by crawling the web and dark web, as well as monitoring communities on platforms such as Telegram, WhatsApp, Instagram, and TikTok. The resulting large-scale, multimodal dataset will reveal attacker tactics and user vulnerabilities. 
  • Phase 2: Prevention Algorithms. The goal is to engineer real-time defenses capable of both recognizing malicious content (e.g., phishing webpages) and identifying contextual anomalies, where user behavior deviates from normal patterns. 

To achieve this, the project will develop a multimodal foundation model for cybersecurity, designed to handle diverse data types (text, images, videos, languages) and tailored specifically to detect and prevent phishing campaigns across multiple attack vectors. 

Required background for the PhD candidate:

  • Strong programming skills, preferably in Python, with experience in deep learning frameworks (e.g., PyTorch, TensorFlow) and data processing tools (e.g., Spark, Pandas). 
  • Solid understanding of Machine Learning and Deep Learning, including model training, evaluation, and deployment. 
  • Knowledge of Natural Language Processing (NLP) and multimodal learning, with familiarity in working with text, images, or video data. 
  • Fundamentals of cybersecurity and networking, including common attack vectors (e.g., phishing, malware, social engineering) and defensive strategies. 
  • Experience in data collection and analysis, such as web crawling, handling large-scale datasets, or working with unstructured data from social platforms.