PhD on Computational Sciences & TechnologICAL Innovation

This curriculum focuses on the synergistic convergence between data-driven Machine/Deep Learning approaches and classical paradigms based on knowledge formalization. 

🔬 Core Research Areas

• Advanced Methodologies and Simulation: Data Analytics, High-Performance Computing (HPC), Network Science, and Multi-Agent Systems Simulation.

• Computational Infrastructure for AI (The Cloud-to-Edge Continuum): The integration of Cloud and Edge Computing into a unified ecosystem, balancing large-scale model training with low-latency, real-time inference at the data source.

•  AI for Health and Life Sciences: The application and development of advanced predictive and generative models for scientific discovery in biomedicine, bioinformatics, and precision medicine.

• Computational Social Science: Interdisciplinary hybridization with socio-economic sciences to analyze and model complex social dynamics using computational methodologies and large-scale data analysis.

PhD candidates will be trained to contribute to both fundamental and applied research (AI for Science), focusing on the development of innovative algorithms, the optimization and security of next-generation computational architectures, and their ethical and societal impact.

This curriculum synergistically integrates the theoretical and experimental dimensions of modern physics, training researchers to operate from mathematical foundations to advanced information processing technologies.

🔬 Core Research Areas

•  Theoretical & Mathematical Foundations: Rigorous modeling of fundamental physical phenomena, quantum mechanics, information theory, particle physics, and complex systems using advanced mathematical tools (e.g., differential geometry, topology).

• Device Physics & Emerging Technologies: Modeling, design, and characterization of nanoelectronic devices, advanced semiconductors, and innovative materials to address the limits of conventional computing.

• Quantum Technologies: A convergence point covering both fundamental quantum information and the development/characterization of physical qubits (e.g., superconducting or silicon-based spin qubits).

In line with European microelectronics and quantum strategies, the program combines solid-state physics, theoretical physics, and advanced mathematics to overcome the limits of current computing through quantum principles, innovative architectures, and emerging materials.