Nanomaster in Green Algorithms for Artificial Intelligence
Nanomaster in Green Algorithms for Artificial Intelligence
Boost your AI career with a sustainable, multidisciplinary approach
The Nanomaster in Green Algorithms for Artificial Intelligence is an innovative training programme designed to train professionals, researchers and students in the development and implementation of AI solutions that are efficient, sustainable and aligned with current technological and social challenges.
Through four specialised and complementary micro-credentials, this nanomaster offers a comprehensive view of the technologies, applications and ethical frameworks that define the new generation of artificial intelligence:
- Design and Implementation – Practical training in cutting-edge technologies such as GPUs and TPUs, algorithm optimisation and sustainable architectures.
- Practical Applications – Case studies and tools in the use of artificial intelligence to address environmental challenges.
- High Performance Computing and Sustainable Robotics – HPC and robotics techniques adapted to energy efficiency and sustainability principles.
- Law, Ethics and Economics – Cross-disciplinary approach to regulatory frameworks, ethical implications and economic sustainability of AI.
This programme is aimed at academic, scientific and professional profiles who wish to specialise in AI from a responsible, technological and applied perspective. Each micro-credential can be taken individually for profiles interested in specialising in this area, obtaining a specific certification for each of them. On completing the four micro-credentials, students will obtain a nanomaster’s degree, which certifies advanced, multidisciplinary training aligned with the Sustainable Development Goals (SDGs).
Who is the target group?
- AI, data or engineering professionals interested in technological sustainability.
- Researchers and academics who want to update their training in efficient AI.
- Master’s or final year undergraduate students with an interest in green technologies.
Get ahead of the future of AI. Train with an ethical, sustainable and high-impact vision.
List of micro-credentials
Microcredential Green Algorithms for Artificial Intelligence: Design and Implementation
Presentation
This micro-credential offers practical and specialised training in the use of cutting-edge technologies, such as GPUs and TPUs, which accelerate the development of machine learning models. Throughout the course, techniques for optimising architectures and algorithms will also be taught, favouring an efficient and sustainable implementation in real environments.
This training is designed to meet the needs of different profiles. In the academic field, it broadens the training offer and promotes technological approaches that are more respectful of the environment. In the scientific context, it trains researchers to innovate in the design of efficient AI models, combining theory and practice. And from a professional perspective, it responds to the growing demand for experts in artificial intelligence with a solid technical background and a clear vision of the challenges of sustainability.
Minimum access requirements
- Completion of secondary school, BUP, FPII or a Higher Training Cycle.
- Knowledge of coding.
Objectives
This micro-credential seeks to train students in the use of artificial intelligence as a tool to promote environmental sustainability and energy efficiency. Students will learn to develop and manage solutions that optimise the use of resources, reduce the ecological footprint and support green initiatives in various industries. Specifically, it seeks to:
- Understand the fundamentals of green AI and its importance for sustainable and efficient technological development.
- Design and implement machine learning models that optimise the use of energy and computational resources.
- Assess the environmental and technical impact of AI models throughout their life cycle, applying sustainability criteria.
- Apply efficient hardware strategies and tools, integrating them into practical solutions in different contexts.
- Communicate and lead sustainable AI projects, conveying their benefits to technical and non-technical audiences.
- Work in interdisciplinary environments and foster continuous learning, adapting to advances in green AI and efficient computing.
Key dates
| Start | End | |
|---|---|---|
| Pre-registration | May 15, 2025 | June 15, 2025 |
| Admission notification | June 2025 | |
| Enrollment | July 1, 2025 | July 15, 2025 |
| Teaching sessions | September 8, 2025 | October 17, 2025 |
| Final assessment | October 2025 |
Contents
- Fundamentals of Machine Learning and Computational Efficiency
Introduction to key concepts in machine learning and deep learning. Types of algorithms, common workflows, and current challenges from a sustainability perspective. Exploration of development environments such as Google Colab and tools for reproducible and efficient experimentation. Comparison of hardware architectures (CPU vs. GPU vs. TPU) from the perspective of energy consumption and scalability.
- Techniques for the Efficient Design of Artificial Intelligence Models
Study of advanced techniques to reduce the computational cost of training and deploying AI models. Strategies such as knowledge distillation, model quantization, the use of compact models, and automated architecture design (NAS) will be covered. Exploration of efficient models in computer vision and natural language processing (NLP), as well as in recommender systems.
- Natural Language Processing with a Sustainable Approach
Application of green AI techniques in natural language tasks such as machine translation, classification, and text generation. Introduction to prompt engineering and methods for efficiently adapting models to specific tasks. Analysis of compact language models and techniques to reduce their computational footprint in real-world applications.
- Specialized Architectures and Emerging Paradigms in Efficient AI
Analysis of emerging architectures such as Mixed Expert Models (MEM) and their applicability in scalable and sustainable systems. Study of techniques such as reinforcement learning and their role in training efficiency. Introduction to the generation of synthetic data for AI using artificially annotated datasets, as well as data augmentation and domain adaptation strategies for training models with fewer resources.
- Graphics Applications and Computer Vision with Green AI
Exploring the use of AI in advanced graphics applications, including 3D Novel-View Synthesis and the generation of synthetic data for training vision models. Analysis of tools and architectures that reduce the computational burden in visual perception and content generation tasks.
Selection Criteria
Preference will be given to students with degrees in Engineering and Architecture.
The assessment of the requirements will be carried out by the Academic Committee of the degree, depending on the form of access, according to the following criteria:
– Academic performance demonstrated during the training stage, with regard to the learning of technical knowledge and basic and specific competences (40%). University transcripts of related degrees (30%), vocational training courses (20%) or technological baccalaureate (10%) will be taken into account. In the case of presenting more than one of these requirements, the highest average will be taken into account. The remaining 10% will be reserved for university master’s degrees.
– Certified knowledge of English as a working language (20 %).
Assessment parameter: development of language skills at an advanced level, sufficient to enable advanced reading comprehension and fluent oral and written communication in academic or professional environments.
To measure this performance, reference will be made to the official English language certificates submitted by the candidates (B1: 5 %, B2: 10 %, C1: 15 % and C2: 20 %).
– Professional excellence and academic motivation assessed on the basis of the curriculum vitae (40 %).
Assessment Method
The method of assessment will consist of:
– Theoretical test to assess understanding of concepts and theories.
– Practical assessments: Projects and practical exercises to demonstrate the ability to apply the knowledge acquired. It is compulsory to attend at least 60% of the face-to-face activities and to obtain a mark higher than 5 in both assessments.
The final grade of the course is composed of 50-100% for the mark obtained in the theoretical exam and 0-50% for the practical part.
Technical data
- Total hours of certified training
150 hours [6 ECTS] [1 ECTS = 25 hours].
- Face to face sessions
Ordinary sessions: Mondays and Wednesdays, from 16:00 to 20:00 h.
Final exam
- Students' autonomous work
Individual work on the preparation of readings and case studies prior to the ordinary sessions and practical workshops.
Individual work in preparation for the final exam.
- Title to which it gives access
Nanomaster in Green Algorithms for Artificial Intelligence
- Number of students
Minimum 12 – maximum 24
- Registration
150 €
- Admission
Nanomaster’s Degree in Green Algorithms for Artificial IntelligenceAdmission to the Micro-credential in Green Algorithms for Artificial Intelligence: Design and Implementation will be final once the Graduate Studies Unit has verified and reported favourably on the application and documentation submitted. Preference will be given to students who have taken other micro-credentials related to the Nanomaster in Green Algorithms for Artificial Intelligence.
- Location
CITIC, Universidade da Coruña
Campus de Elviña s/n – 15071 A Coruña
- Academic management
Brais Cancela Barizo, full university professor, researcher of the UDC-Inditex Chair of AI in Green Algorithms.
- Contact
catedra.inditex.algoritmos.verdes@udc.gal
Green Algorithms for Artificial Intelligence: Practical Applications
Presentation
The microcredential “Green Algorithms for Artificial Intelligence: Practical Applications” addresses the current demand for professionals in data analysis, AI applied to real-world case studies, and statistical quality control within the framework of Industry 4.0 and 5.0, where sustainability plays a key role. The program is based on the expertise of academic staff at the Universidade da Coruña (UDC) in research, teaching, and knowledge transfer to industry, particularly in AI applications for process control, analysis, and improvement (e.g., energy efficiency and sustainability). It aims to enhance the competitiveness of both private companies and public institutions through the practical use of data science techniques.
Minimum access requirements
- Completion of secondary school, BUP, FPII or a Higher Training Cycle.
- Knowledge of coding.
Objectives
This microcredential aims to train professionals working in industry, services, business, and academia, with a focus on practical applications—especially those related to energy efficiency and sustainability. It provides technical, methodological, and applied training in databases, statistical learning, machine learning, and statistical quality control to solve real-world problems. Students will gain competencies in database management, data visualization and analysis, predictive modeling, quality and process control, decision-making, and the use of specialized data science software. Specifically, this microcredential will prepare students to:
- Understand and work with key concepts from Industry 5.0, sustainability, energy use optimization, and efficiency in facilities and buildings, within industrial and business contexts.
- Design and manage databases from a practical perspective.
- Develop skills to identify patterns and improvement opportunities in facilities and processes using exploratory data analysis and visualization techniques.
- Monitor, detect anomalies, identify assignable causes and influential variables in industrial and service processes, and implement continuous improvement procedures using statistical quality control techniques.
- Make predictions, detect patterns, identify groups and anomalies using machine learning techniques that support automation and data-driven decision-making.
- Develop proficiency in using and applying software tools for data science, statistical learning, machine learning, and quality control.
Key dates
| Start | End | |
|---|---|---|
| Pre-registration | June 1, 2025 | June 30, 2025 |
| Admission notification | July 2025 | |
| Enrollment | July 16, 2025 | July 31, 2025 |
| Teaching sessions | November 4, 2025 | December 9, 2025 |
| Final assessment | December 2025 |
Contents
- Introduction to energy efficiency in buildings and facilities
Before working with AI techniques, participants will explore key concepts such as efficiency, energy performance, and sustainability in buildings and infrastructures from a business perspective.
- Database design and management
This unit provides a hands-on introduction to designing and managing databases using specialized software, as a solid database is essential for data analysis and AI applications.
- Data analytics for efficiency and sustainability
Exploratory data analysis, modeling, and computational statistics are used to describe and understand systems, uncover patterns, and support fast, effective decision-making. This unit introduces exploratory analysis, correlation, visualization for decision-making, and various regression models—univariate/multivariate, parametric/non-parametric—with focus on feature selection, predictor importance, prediction, and goodness-of-fit.
- Statistical process control and experimental design
These tools help estimate process level and variability, enabling intuitive anomaly detection in line with Explainable AI (XAI) principles. Practical examples will relate to Industry 5.0, building energy efficiency, and thermal comfort. Key topics include Six Sigma methodology, control charts for univariate, multivariate, and functional data, ANOVA, and experimental design from an industrial and business perspective.
- Machine learning and green algorithms for efficiency and sustainability
Machine learning and green algorithms support the creation of predictive and anomaly detection models that enable intelligent energy optimization in industry and service sectors. This block covers both supervised and unsupervised models, with a focus on practical application of decision trees, Random Forests, Neural Networks, Support Vector Machines, and more.
Selection Criteria
Admission will follow the UDC’s current regulations on entry requirements, as outlined in the Regulations for Lifelong Learning Programs approved by the Governing Council on December 20, 2022: https://www.udc.es/es/uepp/informacion_xeral/normativas/
At a minimum, applicants must hold one of the following: a high school diploma (Bachillerato), BUP, FPII, or an upper-level vocational training certificate. Although not mandatory, it is recommended that students have prior knowledge of programming (R, Python, or similar), statistics, and an interest in sustainability and energy efficiency.
Preference will be given to candidates with degrees in Engineering, Architecture, or Science. The Academic Committee will evaluate applications based on:
– Academic performance during prior education (60%)
– Professional excellence and academic motivation, assessed via CV and cover letter (40%)
Assessment Method
Final evaluation will consist of a multiple-choice exam covering course content and a supervised assignment involving the resolution of a case study. The final grade will be based on 50–100% of the exam score and 0–50% of the supervised project. Attendance to at least 60% of in-person activities is mandatory.
Technical data
- Total hours of certified training
150 hours [6 ECTS] [1 ECTS = 25 hours].
- Face to face sessions
Ordinary sessions: Mondays and Wednesdays, from 16:00 to 20:00 h.
Final exam
- Students' autonomous work
Individual work on the preparation of readings and case studies.
Individual work in preparation for the final exam.
- Title to which it gives access
Nanomaster in Green Algorithms for Artificial Intelligence
- Number of students
Minimum 12 – maximum 24
- Registration
150 €
- Admission
Admission to the Micro-credential in ‘Green Algorithms for Artificial Intelligence: Practical Applications’ will be final once the Graduate Studies Unit has verified and reported favourably on the application and documentation submitted. Preference will be given to students who have taken other micro-credentials related to the Nanomaster in Green Algorithms for Artificial Intelligence.
- Location
CITIC, Universidade da Coruña
Campus de Elviña s/n – 15071 A Coruña
- Academic management
Javier Tarrío Saavedra, full university professor, researcher of the UDC-Inditex Chair of AI in Green Algorithms.
- Contact
catedra.inditex.algoritmos.verdes@udc.gal