PhD Program in Computer, Language and Data Science for social innovation

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, and (iii) state-of-the-art and emerging application domains, while fostering cross-disciplinary synergies between scientific and social fields. Read more

The PhD program also includes a specialized research area dedicated to the study of human language.

Natural language syntax – Modelling cultural diversity in language and cognition

3 positions available for the Academic Year 2026/2027!
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Researchers will be trained in the analysis and quantitative modeling of formal language data.

Goals

(a) investigate and explain language diversity through formal models and state-of-the-art comparative techniques
(b) explore crosslinguistic variation through structured system of abstract rules
(c) develop dedicated algorithms to extract historical signals from syntactic parameters
(d) investigate the phylogenetic structure of syntactic diversity using computational and quantitative methods
(e) develop data-driven models of the structure of human grammars

Research activities

(a) analysis of cross-linguistic datasets, formalization of syntactic variation, comparative studies across language families
(b) construction and testing of rule-based models, analysis of parametric systems, development of formal representations of variation
(c) design and implementation of computational tools, processing of syntactic datasets, validation of extracted historical patterns
(d) reconstruction of language relationships, application of phylogenetic models, quantitative analysis of syntactic data
(e) training and evaluation of computational models, analysis of large-scale linguistic data, integration of formal and statistical approaches

Who can apply

(a) Candidates with a background in computer science (e.g., machine learning, algorithms, deep learning, AI) interested in novel applications of computer-aided techniques to formal and historical research in linguistics
(b) Candidates with a background in linguistics (e.g., computational, formal, historical linguistics, psycholinguistics, neurolinguistics, language acquisition) interested in exploiting computational and quantitative approaches to the analysis of human language

Teaching staff

Courses and seminars, a.a. 2026/2027 (preliminary draft)