ML3T-SCI programme
Integrated teaching and training activities
This international Master’s programme in Human Language Technologies is structured around a coherent learning path combining one common foundational track, two alternative specialisation tracks, and a final integrative track, delivered across Europe and Latin America to offer students a truly global academic experience.
ML3T-SCI programme overview
The ML3T-SCI’s academic design will enable students to acquire the necessary skills in the development and application of Large Language Models (LLM) and Generative AI, either to deepen their scientific vocation or to enhance process management in business or public administration. The programme has been planned for students to earn the accreditation of 90 ECTS credits in three semesters: a common initial one (30 ECTS), an intermediate specialised one (30 ECTS) to choose between scientific or business innovation orientation, and a final common one (30 ECTS) with the practical applications considering the double perspective of the masters. This ensures that students receive their training in three different countries, with the incentive of cultural immersion, inclusion, and equal opportunities.
It begins with a foundational track at the University of Alicante (Spain), providing a solid grounding in Artificial Intelligence, Machine Learning, Deep Learning, and Natural Language Processing.
Students then specialise in either
- a scientific research track at the University of Havana (Cuba), focused on advanced language models, AutoML, and intelligent agents,
- or a business and innovation track at the Universidad Industrial de Santander (Colombia), centred on entrepreneurship, innovation management, and technology transfer.
The programme concludes with an applied and integrative track at the International Hellenic University (Greece), where students work on real-world applications such as generative AI, information retrieval, knowledge graphs, and business intelligence, culminating in the development and defence of the Master’s thesis.
Together, these tracks provide a clearly structured, jointly designed EMJM pathway that integrates complementary expertise from European and Latin American partners in line with EU digital priorities.
Graduates acquire advanced technical skills, research and innovation capabilities, and strong experience working in multicultural and interdisciplinary environments, preparing them for global careers in academia and industry. In particular, the programme’s design ensures constructive alignment between mobility, specialisation tracks, and intended learning outcomes, so that students progressively develop the competencies required to become highly employable experts in language-based AI systems and services.
Semester distribution
The first semester is common to all students and is taught entirely at the University of Alicante (UA). During the second semester, students may choose between two paths: the University of Havana (UH) or the Industrial University of Santander (UIS), which offer a scientific or business focus, respectively. Finally, the third semester returns to a single location, with all subjects delivered at the International Hellenic University (IHU). This integrated mobility scheme ensures both depth of specialisation and strong programme cohesion across the consortium.
The EMJM is grounded in existing, accredited Master’s programmes. The most specialised and academically robust modules in Natural Language Processing and Language Technologies have been selected according to their relevance, excellence, and complementarity, thereby ensuring a high-quality, research-informed curriculum. In particular, each institution’s contribution is based on the following programmes:
- UA: Master’s Degree in Artificial Intelligence (60 ECTS)
- UH: Master’s Degree in Computer Science (60 ECTS)
- UIS: Master’s Degree in Industrial Engineering (90 ECTS)
- IHU: Master’s Degree in Data Science (xx ECTS)
In all cases, alongside the subjects taught as part of the curriculum, students are allocated ECTS credits to develop their Master’s Thesis, which are distributed evenly across the three semesters. The thesis is closely linked to the programme’s research and innovation objectives and is often carried out in collaboration with research centres or industry partners, strengthening both academic excellence and employability.
Institutional Credit Breakdown
The following subsections present the institutional credit breakdown for the ML3T‑SCI programme, detailing how the 90 ECTS are distributed across partner universities, semesters, and modules. This breakdown makes explicit how each institution contributes complementary expertise to the common curriculum and how thesis‑related credits are embedded throughout the three semesters.
Semester 1. Fundamentals of Natural Language Processing
Institution: UA
ECTS: 30
| SUBJECT | ECTS |
|---|---|
| 1.1 Machine Learning Techniques | 4.5 |
| 1.2 Artificial Intelligence Techniques | 4.5 |
| 1.3 Applications of Natural Language Processing | 4.5 |
| 1.4 Deep Learning | 4.5 |
| 1.5 Natural Language Processing Techniques | 4.5 |
| 1.6 Master Thesis Project Preparation | 7.5 |
Semester 2. Science-orientated Human Language Technologies track
Institution: UH
ECTS: 30
| SUBJECT | ECTS |
|---|---|
| 2A.1 Deep Learning for NL | 3.5 |
| 2A.2 Language Models – Concepts and Techniques | 3.5 |
| 2A.3 Advanced Techniques and Applications for Language Models | 3.5 |
| 2A.4 Socratic Agents | 3.5 |
| 2A.5 AutoML for Language Models | 3.5 |
| 2A.6 Master Thesis Project Preparation IIA | 12.5 |
Semester 2. Human Language Technologies applied to Corporate Innovation track
Institution: UIS
ECTS: 30
| SUBJECT | ECTS |
|---|---|
| 2B.1 Creation and management of technology-based companies | 4 |
| 2B.2 Innovation Management and Innovation Systems | 4.5 |
| 2B.3 Valuation and technology transfer | 4.5 |
| 2B.4 Technological foresight | 4.5 |
| 2B.5 Master Thesis Project Preparation IIB | 12.5 |
Semester 3. Human Language Technologies and potential applications to be implemented in the business world
Institution: IHU
ECTS: 30
| SUBJECT | ECTS |
|---|---|
| 3.1 Advanced Machine Learning | 4 |
| 3.2 Information Retrieval | 4 |
| 3.3 Knowledge Management in the Web | 4 |
| 3.4 Data Science for Business: Theory and Practice | 4 |
| 3.5 Programming for Data Science | 4 |
| 3.6 Master Thesis Project | 10 |
Overall, this credit structure ensures that all students complete a coherent 90‑ECTS trajectory combining a shared foundational semester, a science‑oriented or business‑innovation specialisation, and a final integrative semester, with the Master’s Thesis component progressively developed across all partner institutions.
