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Course presentation (FOR PhD course/Specialisation school ONLY)
The PhD Program in Computer, Language and Data Science for Social Innovation (CLDS4SI) is designed to provide advanced training across the core areas of Computer and Language Sciences, with particular emphasis on Data Science applications and on comparative language analysis.
Supported by a highly qualified, multidisciplinary PhD program board, an extensive network of national and international collaborations, long-standing partnerships with research centers and industry, and the participation in a large number of funded research projects, the program aims to train highly qualified researchers and professionals by combining solid theoretical and technical expertise with complementary soft skills.
The program covers both theoretical and practical aspects, including:
- (i) Core technologies and methods in computer science;
- (ii) Formal and historical approaches to the study of human language;
- (iii) State-of-the-art and emerging application domains, while fostering cross-disciplinary synergies between scientific and social fields.
Among the core computer science technologies, the program emphasizes:
- Distributed and parallel systems (including high-performance computing and embedded systems, Internet of Things, and edge/cloud computing, complex systems modeling);
- Scalable data science and data-centric AI;
- Cybersecurity.
Concerning natural language science, the novelty of this doctoral program lies in the integration of theoretical linguistics with computer science. The program focuses on the comparative and formal analysis of natural language syntax, with particular emphasis on phylogenetic modeling and the measurement of language relatedness.
By developing mathematical and computational models of the phylogenetic and formal structure of human language syntax, the program provides an integrated framework for analyzing the structure and evolution of human languages.
The relevant application domains are numerous and strongly rooted in the digital transformation in two main areas:
- Social areas: Recommendation systems, sharing economy, social contagion and viral marketing, human-machine interaction, business ethics, natural language modeling, business analytics, digital preparedness;
- Technological areas: Industry 4.0, Autonomic computing for collective self-adaptive systems, Autonomous Systems and Connected Shared Mobility, clinical medicine.
The training program, updated annually and advertised on the course webpage, combines specialized courses and seminars, summer schools, and active involvement in research activities.
The aim is to foster scientific outcomes, the participation in international conferences, as well as research stays and internships at external institutions abroad. These experiences enable students to:
- Consolidate their scientific profile;
- Refine their research methodology;
- Build a network of scientific contacts that will support their post-doctoral careers.