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The PhD in SCIENCE AND TECHNOLOGY FOR ELECTRONIC AND TELECOMMUNICATION ENGINEERING (STIET), is promoted by the Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN) in agreement with the Italian Institute of Technology (IIT).
Ph.D. STIET Course is coordinated by Prof. Maurizio Valle and it is organized into three curriculum:
- Electromagnetism, Electronics, Telecommunications;
- Interactive Cognitive Environments;
- Computational vision, recognition and machine learning (developed entirely at IIT)
Each student, if foreseen by his / her training plan, will be provided with an access to databases for the development of his / her research, a space within a research laboratory and one (or more, one according to the needs) processing and programming tools so that the research work can be carried out effectively.
STIET has very precise didactic and scientific rules (reported on the PhD website) which are transmitted to the students at the beginning of the PhD Course and are mandatory to be admitted to the next year and, at the end of the PhD, to the final exam. The PhD student is assigned with one or more Tutors, first responsible for the student's learning and scientific activity. At least one of the Tutors must be a university professor or a member of the Teaching Board.
STIET Teaching Board involves carefully selected international experts.
Announcement
Year 2021 Ph.D. Programme
Italian XXXVII cycle
Deadline for application June 15, 2021 at 12 PM, CET
https://unige.it/en/usg/en/phd-programmes
Research Themes 2021
Electromagnetism, Electronics, Telecommunications and Interactive Cognitive Environments curriculum themes:
- Imitation learning in multisensor self-aware autonomous systems - (tutor Prof. L. Marcenaro)
- Cooperative self-awareness for AI enabled cognitive radio - (tutor Prof. C. Regazzoni)
- Underwater acoustic systems for the analysis of natural and anthropogenic phenomena (tutor Prof. A. Trucco)
- Signal and Image processing as a support for diagnosis and telerehabilitation (tutor Prof. S. Dellepiane)
- Multiscale modeling in high-frequency electromagnetics for photobiomodulation and two-dimensional material applications (tutor Prof. M. Raffetto)
- Deep learning models for predicting automated driving scenarios (tutor Prof. F. Bellotti)
- Edge/cloud big data management, analytics and machine learning (tutor Prof. R. Berta)
- Autonomous agents based on deep reinforcement learning (tutor Prof. A. De Gloria)
- Novel inversion techniques for electromagnetic imaging (tutor Prof. M. Pastorino)
- Design and development of resource-constrained systems for machine learning (tutor P. Gastaldo, R. Zunino)
- Robotic systems based on tactile sensing for biomedical applications (tutor Prof. L. Seminara)
- Innovative electronic systems for medical diagnostics in neurology (tutor Prof. L. Seminara)
- Multilinear Regression Methods for IoT Applications in the framework of e-health services (tutor Prof. F. Lavagetto)
- Achieving wisdom THrough channel stateE information analysis for coNtext-Aware device-free applications (tutor Prof. A. Sciarrone)
- Self-similar Radio Signal Analysis for IoT Device Detection applied to Drones for Security Frameworks (tutor Prof. I. Bisio)
- New-generation Control and Management approaches for distributed and cloud-native 5G Radio Mobile Networks (tutor Prof. R. Bruschi)
- Cybersecurity in 5G Radio Mobile Networks and Industry 4.0 Vertical Applications (tutor Prof. R. Bolla)
- Probabilistic graphical models and machine learning for remote sensing image analysis (tutor Prof. S. B. Serpico, Prof. G. Moser)
- Embedded edge computing machine learning for high density sensor arrays in autonomous systems (tutor Prof. M. Valle)
- Cybersecurity threats analysis in Satellite Networks (tutor Prof. M. Marchese)
- Resource Allocation in Satellite Networks through Virtualization (tutor Prof. M. Marchese)
- Investigation, implementation and testing of experimental systems capable of correlating physical and cyber security events (funded by IREN SpA )
- State of the art, development and performance analysis of algorithms for the correlation of events for the detection, prevention and reaction to attacks and anomalies (Executive PhD, IREN SpA )
- Investigation, implementation and testing of innovative solutions aimed at improving the security level of currently used systems (partially funded by SABABA SECURITY SRL)
Computer Vision, Pattern Recognition and Machine Learning:
- Theme A: 3D scene understanding with geometrical and deep learning reasoning
- Theme B: Artificial Intelligence for Human Behavior Analysis
- Theme C: Deep Learning for Multi-modal scene understanding
- Theme D: Self-Supervised and Unsupervised Deep Learning
- Theme E: Visual Reasoning with Knowledge and Graph Neural Networks for scene understanding
More detailed information on the specific themes and indication of supervisors, at
https://pavisdata.iit.it/data/phd/2022_PhD_research_topics_PAVIS_IIT.pdf