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	15 May 2025	15 June 2025
Communication of admitted students	June 2025
Matriculation	1 July 2025 2025	15 July 2025
Lecture sessions	8 September 2025	17 October 2025
Objective test	October 2025

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.

Technical data

150 hours [6 ECTS] [1 ECTS = 25 hours].

Ordinary sessions: Mondays and Wednesdays, from 16:00 to 20:00 h.

Final exam

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.

Nanomaster in Green Algorithms for Artificial Intelligence

Minimum 12 – maximum 24

150 €

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.

CITIC, Universidade da Coruña

Campus de Elviña s/n – 15071 A Coruña

Brais Cancela Barizo, full university professor, researcher of the UDC-Inditex Chair of AI in Green Algorithms.

catedra.inditex.algoritmos.verdes@udc.gal

Nanomaster in Green Algorithms for Artificial Intelligence