IIT Annual Academic Report CampaignCourse 2023 - 2024 - English Version
The information available of Dr.D.José Portela González since September 01, 2023 to August 31, 2024 is showed below. This information will be used to develop the IIT Annual Academic Report and the Comillas Annual Report, so it is very important that it is correct and complete. All fields displayed in each reference must have information, unless otherwise stated (eg "Not Applicable"). Please, complete it. Also make sure the information is displayed in the correct section. If you find any errors or missing information, please send an email by clicking here indicating the section and incorrect or missing information. To send comments/suggestions/bugs... on the website, please click here. To view the Spanish Version, click here. To view the Full Version of the report, click here.
[Expand all | Collapse all] Chapter 1. Organizational structure [ |  ]- 1.1 Management
- 1.2 Council
- 1.3 Area coordinators
| * | Información pendiente de formato. | | | | |
- 1.4 Scientific advisory board
- 1.5 Academic staff
| * | Name: | José Portela González | | | Category: | Associate Professor | | | Degree: | PhD in Engineering (Comillas), Electronics Engineer (Comillas), M.Sc. in Research in Engineering Systems Modeling (Comillas) | | | Areas of Interest: | Functional Data Analysis, Machine Learning, Neural Networks, time series models | | | ICAI Department: | DEAC | | | | |
- 1.6 Associated academic staff
- 1.7 Pre- and postdoctoral fellows
- 1.8 Areas of Research
- 1.8.1 MAC Coordinator
- 1.8.2 REDES Coordinator
- 1.8.3 RYE Coordinator
- 1.8.4 SADSE Coordinator
- 1.8.5 PCI Coordinator
- 1.8.6 ASF Coordinator
- 1.8.7 ASI Coordinator
| * | Name: | José Portela González | | | | |
- 1.8.8 BIO Coordinator
- 1.8.9 SMS Coordinator
- 1.9 Services staff
- 1.9.1 Systems Administrator Staff
- 1.9.2 Administrative Staff
Chapter 2. Research [ | ]- 2.1 Research Projects
- 2.1.1 Research and develop projects
- 2.1.1.1 Private funding
| * | Abbreviated name: | INNOMERICS_ATMOSPHERE_2023 | | | Project title: | ATMOSPHERE. New methodologies for the storage, generation and safety of green hydrogen plants | | | Funding entity: | Innomerics S.L | | | Start date: | January 2023 | | | End date: | June 2025 | | | Researcher(s): | José Portela González, Alejandro Polo Molina | | | Description: | The project consists in the construction of a digital twin of a green hydrogen production plant and its validation with real operation data. IIT contributes in the development of the library of mathematical models representative of the different elements that constitute an industrial green hydrogen production plant and in the subsequent integration of these models with scientific machine learning algorithms, in order to merge models based on physical equations with operational data using machine learning techniques.
In addition, IIT approaches the definition of the input data for the design of heat networks to take advantage of the waste heat from hydrogen production plants, as well as in their storage format and in the research of the algorithms for the calculation of the necessary models.
Project of the Science and Innovation Missions Program 2022 of the State Program to Catalyze Innovation and Business Leadership of the State Plan for Scientific Research and Innovation 2021-2023 within the framework of the Recovery, Transformation and Resilience Plan financed by the Ministry of Science and Innovation (MIG-202210 | | | | | | * | Abbreviated name: | ENDESA_PROTECCIONES_2023 | | | Project title: | Design and implementation of an algorithm for power line protection under high penetration of renewables | | | Funding entity: | Gas y Electricidad Generación S.A. | | | Start date: | April 2023 | | | End date: | December 2023 | | | Researcher(s): | Antonio Muñoz San Roque, Luis Rouco Rodríguez, Lukas Sigrist, José Portela González | | | Description: | The objective of this project is the design and implementation of an algorithm for the protection of power lines in conditions of high penetration of RES.
The proposed algorithm is based on the application of Machine Learning techniques for the detection and classification of faults based on a set of fault scenarios obtained both from real records and through simulation. | | | | | | * | Abbreviated name: | Proyecto_ACM_2023_05 | | | Project title: | Analysis and prediction of offshore wind farm generation using machine learning techniques | | | Funding entity: | Comillas | | | Start date: | June 2023 | | | End date: | May 2024 | | | Researcher(s): | Antonio Muñoz San Roque, José Portela González, Eugenio Francisco Sánchez Úbeda, Eloy Jesús del Gran Poder Insunza Díaz | | | Description: | The objective of this research is twofold. On the one hand, meteorological data as well as wind turbine data will be analyzed. On the other hand, power generation prediction models will be created using different AI algorithms. For the study, wind data from the Alpha Ventus wind farm off the coast of Hamburg will be used. Finally, the prediction results using different algorithms will be compared. | | | | | | * | Abbreviated name: | SUBSTRATEAI_2023 | | | Project title: | Artificial Intelligence for Energy Saving in Hotels | | | Funding entity: | Substrate AI, S.A | | | Start date: | September 2023 | | | End date: | May 2024 | | | Researcher(s): | José Portela González, Francisco Martín Martínez, Samuel Peña Yunda | | | Description: | This project aims to implement an Artificial Intelligence system for temperature control in hotels, with the goal of energy conservation. Environmental and cooling system data are analyzed, and clustering algorithms are employed to detect patterns. Using this insight, predictive models are developed for key temperature management variables. An optimization model is constructed to enhance hotel comfort, taking into account current and predicted variables. A software is deployed, integrating predictive and optimization models to achieve desired room temperatures in the hotel. | | | | | | * | Abbreviated name: | HADES_2023 | | | Project title: | Development of a functional time series model for forecasting residual demand curves in the Spanish day-ahead market | | | Funding entity: | Endesa Medios y Sistemas S.L | | | Start date: | September 2023 | | | End date: | December 2023 | | | Researcher(s): | José Portela González, Antonio Muñoz San Roque, Alejandro Polo Molina | | | Description: | The aim of the proposed collaboration is the development and implementation of a deterministic prediction model for residual demand curves in the day-ahead market. The model is a functional time series prediction model specifically designed for curve predictions in the market, taking into account the impact of the most relevant explanatory variables, as well as the temporal dynamics of the offers. | | | | | | * | Abbreviated name: | CIAMOD | | | Project title: | Applications of computational methods and artificial intelligence to the study of moduli spaces | | | Funding entity: | Convocatoria de Financiación de Proyectos de Investigación Propios 2023 | | | Start date: | September 2023 | | | End date: | August 2026 | | | Researcher(s): | David Alfaya Sánchez, José Portela González | | | Description: | It is common to find applications of mathematics to AI and computation. Recent breakthroughs like ChatGPT have increased the interest on the opposite process: the ability of computers to help proving new theorems. This project deepens in this question, exploring the following lines of research in which AI and computation are used to study open problems related with moduli spaces which are very relevant in the current mathematical landscape.
Analysis of the stability chambers of the moduli space of parabolic bundles:
The geometry of these moduli spaces depends on certain weights which are chosen for their construction. These are grouped in stability chambers in which the moduli space remains constant, represented by regions in a hypercube and there exist transformations identifying moduli from certain different chambers. Our goal is to estimate the number of different moduli spaces that there exist and to study their birational geometry through the study of the geometry of these chambers with computer assisted techniques.
Decompositions of motives of moduli:
Motives are invariants which provide a lot of geometric information. Manipulating motivic formulas and understanding when can two different expressions represent the same variety is a problem of great interest. We will develop a software package for manipulating and comparing motives efficiently and we will apply it to Mozgovoy’s conjecture on the L-Higgs moduli and to obtain formulas with positive coefficients for the moduli of vector bundles.
Study of Markoff m-triples and its relation to Higgs moduli:
Markoff triples are integral solutions to x^2+y^2+z^2=3xyz. They are structured in trees, subject of the famous Markoff Conjecture. m-triples are solutions to x^2+y^2+z^2=3xyz+m and their theory is jet to be developed. We will use computational techniques to research conjectures about its structure and, starting from the relations between triples and points in the moduli of representations, we will explore the relations between m-triples and the Higgs moduli. | | | | | | * | Abbreviated name: | HADES_2024_S1 | | | Project title: | Forecasting and characterization of offer strategies in the Spanish real-time constraint market | | | Funding entity: | Endesa SA: Energy & Commodity Management Iberia | | | Start date: | March 2024 | | | End date: | July 2024 | | | Researcher(s): | José Portela González, Antonio Muñoz San Roque, Eloy Jesús del Gran Poder Insunza Díaz | | | Description: | This project aims to identify an optimal strategy for modeling and predicting the allocation of groups in the Spanish real-time constraint market and its Portuguese equivalent. To achieve this, residual demand curves will be established to characterize the offer strategy of agents in these markets, characterized by being a "Pay as Bid" system. | | | | |
- 2.1.1.2 Public funding
| * | Abbreviated name: | RETOSCOL-RC4ALL-2020 | | | Project title: | RC4ALL: Responsible consumption for all | | | Funding entity: | Ministerio de Ciencia e Innovación (MCI), Agencia Estatal de Investigación (AEI) | | | Start date: | May 2020 | | | End date: | December 2023 | | | Researcher(s): | Eugenio Francisco Sánchez Úbeda, Antonio Muñoz San Roque, José Portela González, Ignacio Navas Pascual, Francisco Rodríguez Cuenca | | | Description: | The main objective of the RC4ALL project (Responsible Consumption for All) is to develop a system that, based on the specific information on consumption per device of a relatively small number of representative customers and complementing it with information from external sources, is capable of generating personalized recommendations that improve the efficiency of consumption for the entire customer base of the company. Machine Learning and Big Data techniques will be used.
Project Retos- Colaboración RTC2019-007380-3 funded by Ministerio de Ciencia e Innovación (MCI) and Agencia Estatal de Investigación (AEI) 
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| | | | | | * | Abbreviated name: | RETOSINV-chronic-IoT-2020 | | | Project title: | Development of movement behavior models of complex chronic patients | | | Funding entity: | Ministerio de Ciencia e Innovación (MCIN), Agencia Estatal de Investigación (AEI) | | | Start date: | June 2020 | | | End date: | November 2023 | | | Researcher(s): | Eugenio Francisco Sánchez Úbeda, Rafael Palacios Hielscher, Antonio Muñoz San Roque, José Portela González, Carlos Rodríguez-Morcillo García, Alejandro Polo Molina | | | Description: | The aim of this project, coordinated with Virgen del Rocío University Hospital (HUVR), is to investigate how the deterioration of mobility may reflect changes in the patient's clinical condition, and its degeneration in the domain of integrated care of complex chronic patient.
To fulfill this objective, an IoT infrastructure and information system is developed. Based on the collected data on patients mobility, machine learning techniques are applied to create patterns capable of modeling and characterizing movement in the patients in order to explain aspects of the clinical evolution of patients.
Project PID2019-110747RB-C22/ funded by MCIN/AEI/10.13039/501100011033
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| | | | | | * | Abbreviated name: | TwinEU | | | Project title: | Digital Twin for Europe | | | Funding entity: | European Commission | | | Start date: | January 2024 | | | End date: | December 2026 | | | Researcher(s): | Javier Matanza Domingo, José Pablo Chaves Ávila, Néstor Rodríguez Pérez, Gregorio López López, Miguel Ángel Sánchez Fornié, José Portela González, Carlos Mateo Domingo | | | Description: | The TwinEU project is at the forefront of transforming the energy sector through a series of innovative actions aimed at enhancing the capabilities, interoperability, and performance of Digital Twins (DTs) across Europe.
- Cross-Domain Federated DTs: Bridging the gap between different DTs by utilizing existing Smart Grid standards, open APIs, and integration with TensorFlow Hub, fostering a seamless exchange of data and models across various stakeholders.
- HPC-coupled Federated DTs Infrastructure: Leveraging High-Performance Computing (HPC) to boost the computational power of DTs, ensuring faster and more accurate data processing for real-time applications.
- Dataspace Adaptation for Pan-European DTs: Facilitating the sharing and exchange of AI/ML models and data across Europe, with a focus on improving interoperability, lifecycle management, and cross-stakeholder collaboration.
- Closed Loop Adaptive Digital Twins: Evolving DTs to be context-aware, autonomous, and adaptive, capable of real-time decision-making and self-learning to enhance grid monitoring and operation.
- Immersive Metaverse-oriented DTs: Integrating DTs with immersive technologies to create user-friendly and engaging Metaverse-oriented experiences, improving workflows and stakeholder collaboration in smart grids.
The project's objective is to set the stage for a more integrated, efficient, and innovative energy sector, paving the way for smarter grids and a sustainable future. | | | | |
- 2.1.2 Consultancy and technological support
- 2.1.2.1 Private funding
| * | Abbreviated name: | HADES_KT_MD_2023 | | | Project title: | Migration of the residual demand curve prediction model in the day ahead market.to the Cloud environment | | | Funding entity: | Endesa Medios y Sistemas S.L | | | Start date: | June 2023 | | | End date: | September 2023 | | | Researcher(s): | José Portela González, Alejandro Polo Molina | | | Description: | The main objective of this project is to migrate the residual demand curve forecasting model in the Spanish day-ahead market to the cloud environment. Taking as inputs different variables of the electricity market, the model applies dimension reduction techniques together with regression models to estimate the curves. | | | | | | * | Abbreviated name: | MTO-COR-CODEX-SIROCO-DESI-2024 | | | Project title: | Assistance and maintenance of tools CODEX, SIROCO and DESI | | | Funding entity: | Endesa Medios y Sistemas S.L | | | Start date: | January 2024 | | | End date: | December 2024 | | | Researcher(s): | Francisco Alberto Campos Fernández, Efraim Centeno Hernáez, Luis Alberto Herrero Rozas, Enrique Lobato Miguélez, Javier García González, José Portela González | | | Description: | Assistance and maintenance of tools CODEX, SIROCO-Ofertas Y DESI developed by IIT for Endesa | | | | | | * | Abbreviated name: | SantaLucia_App_2024 | | | Project title: | development of an interactive application for data analysis using neural networks and the neuralsens methodology for interpretability | | | Funding entity: | Cátedra Santalucía de Analytics for Education | | | Start date: | April 2024 | | | End date: | June 2024 | | | Researcher(s): | Jaime Pizarroso Gonzalo, José Portela González | | | Description: | This project is a collaborative initiative proposed by the Instituto de Investigación Tecnológica (IIT) with the Cátedra Santalucía de Analytics for Education.
The project's main goal is to create a functional application designed in R, allowing interactive data analysis. The application will facilitate the understanding of complex datasets through advanced neural network techniques and the NeuralSens interpretability framework. This tool aims to support educational and research purposes, particularly for students and researchers in data science fields.
The application will be distributed among students at Universidad Pontificia Comillas and Cátedra Santalucía, supporting their learning and research activities. It will facilitate advanced data analysis and model interpretability, contributing to the fields of machine learning and data science.
Key Features and Deliverables
- Interactive Analysis: The application will include features for importing data, training linear and neural network models, and performing sensitivity analysis.
- NeuralSens Methodology: Incorporates the NeuralSens methodology for detailed data interpretation and model understanding.
- User Interface: The tool will have a user-friendly interface with components such as a top bar for primary functions, a sidebar for dataset and model management, and a main panel for detailed data views and model summaries.
- Data Import and Preparation: Supports CSV and XLSX file formats for data import, with detailed guides for data preparation to ensure compatibility and ease of use.
- Training and Analysis: Provides functionalities for training both linear and neural network models, with options for advanced configurations like cross-validation and hyperparameter tuning. | | | | | | * | Abbreviated name: | HADES_KT_MD_2024 | | | Project title: | Migration.to the cloud environment of the GBD Database for evaluating predictive models | | | Funding entity: | Endesa Medios y Sistemas S.L | | | Start date: | May 2024 | | | End date: | July 2024 | | | Researcher(s): | José Portela González | | | Description: | The main objective of this project is to migrate the data storage system to the cloud environment for the execution of forecast models of residual demand curves in the Spanish daily market. | | | | |
- 2.1.2.2 Public funding
- 2.1.3 Services and analysis projects
- 2.1.3.1 Private funding
| * | Abbreviated name: | MTO-DECA-MODEM-HADES-EXLA-EXCOM-2023 | | | Project title: | Technical support for the tools DECA, HADES and MODEM | | | Funding entity: | Endesa Medios y Sistemas S.L | | | Start date: | January 2023 | | | End date: | December 2023 | | | Researcher(s): | Eugenio Francisco Sánchez Úbeda, José Portela González, Javier García González | | | Description: | The objective of this project is to provide ENDESA with technical support and maintenance of the tools DECA, MODEM, HADES, EXLA and EXCOM developed by IIT. | | | | |
- 2.1.3.2 Public funding
- 2.2 Publications
- 2.2.1 Books
- 2.2.2 Chapters in books
| * | Author(s): | A. Muñoz, J. Portela, E.F. Sánchez-Úbeda, G. Mestre | | | Chapter title: | Análisis y predicción de curvas agregadas de oferta y demanda en el mercado eléctrico europeo | | | Book title: | Predicción y decisiones económicas con Big Data | | | Editors: | Peña Sánchez de Rivera, Daniel; et al., | | | Publisher: | Fundación de las Cajas de Ahorros (FUNCAS) | | | Pages: | 185-224 | | | ISBN: | 978-84-17609-79-5 | | | Publication date: | June 2024 | | | | |
- 2.2.3 Papers in Journals
| * | Author(s): | C. Álvarez-Romero, A. Polo-Molina, E.F. Sánchez-Úbeda, C. Jiménez-de-Juan, M.P. Cuadri-Benitez, J.A. Rivas-González, J. Portela, R. Palacios, C. Rodríguez-Morcillo, A. Muñoz, C.L. Parra-Calderón, M.D. Nieto-Martin, M. Ollero-Baturone, C. Hernández-Quiles | | | Title: | Machine learning–based prediction of changes in the clinical condition of patients with complex chronic diseases: 2-phase pilot prospective single-center observational study | | | Journal: | JMIR Formative Research | | | Vol & Num: | vol. 8, nº. 1 | | | Pages: | e52344-1-e52344-13 | | | ISSN: | 2561-326X | | | Publication date: | April 2024/December 2024 | | | | | | * | Author(s): | J. Portela, D. Roch Dupré, I. Figuerola-Ferretti Garrigues, C. Yéboles, A. Salazar | | | Title: | Monitoring the green transition in the power sector with the electricity generation emissions (EGE) tracker | | | Journal: | Energy Strategy Reviews | | | Vol & Num: | vol. 50 | | | Pages: | 101236-1-101236-15 | | | ISSN: | 2211-467X | | | Publication date: | October 2023/November 2023 | | | | |
- 2.2.4 Papers in Congress
| * | Author(s): | G. Mestre, J. Portela, A. Muñoz, E. Alonso | | | Title: | Probabilistic functional forecasting of Residual Demand Curves in electricity markets | | | Type: | Communication | | | Congress: | 26th International Conference on Computational Statistics - COMPSTAT 2024 | | | City: | Giessen | | | Country: | Germany | | | Date: | 27-30 August 2024 | | | ISBN: | | | | | |
- 2.2.5 IIT Technical Documents
- 2.2.6 Other Publications
- 2.3 Software Products
Chapter 3. Teaching [ | ]- 3.1 Supervised Bachelor Theses at IIT
- 3.1.1 Bachelor's Degree in Engineering for Industrial Technologies
| * | Title: | Diseño y caracterización de un generador sintético de planos para el entrenamiento de un modelo de Deep Learning | | | Author: | Iglesias Aramburu, Galo | | | Supervisor(s): | Álvaro Jesús López López, José Portela González | | | | |
- 3.1.2 Bachelor's Degree in Engineering in Telecommunications Technologies
- 3.2 Postgraduate Teaching
- 3.2.1 Master Courses
- 3.2.1.1 Official Master's Degree in Industrial Engineering (MII)
- 3.2.1.2 Official Master's Degree in Telecommunications Engineering (MIT)
- 3.2.1.3 Official Master's Degree in the Electric Power Industry (MEPI)
- 3.2.1.4 Master in Railway Systems (MSF)
- 3.2.1.5 Master in Project, Construction and Maintenance of High Voltage Electrical Transmission (On-line)
- 3.2.1.6 Master in Fire Protection Engineering (MIPCI)
- 3.2.1.7 MBA in the Global Energy Industry
- 3.2.1.8 Master's Degree in Smart Industry (MIC)
- 3.2.1.9 Master’s Degree in International Industrial Project Management
- 3.2.1.10 Master in Electricity Generation. Promotion, Technology and Operation (MGE) (On-line)
- 3.2.1.11 Master's Degree in Big Data Technologies and Advanced Analytics (MBD)
- 3.2.1.12 Master's Degree in Management Solutions (MCN)
- 3.2.1.13 Master's Degree in Smart Grids (MSG)
- 3.2.1.14 Master in Biomechanics Applied to Damage Assessment; Advanced Physical Therapy Techniques
- 3.2.1.15 Master in Mobility and Safety Engineering (MMS)
- 3.2.1.16 Master in Electrical Technology (MTE)
- 3.2.1.16 Master in smart agricultural industry and sustainability
- 3.2.1.17 Official Master's Degree in Biomechanics and Sports Physiotherapy
- 3.2.2 Master Theses supervised at IIT
- 3.2.2.1 Official Master's Degree in Industrial Engineering (MII)
- 3.2.2.2 Official Master's Degree in Telecommunications Engineering (MIT)
- 3.2.2.3 Official Master's Degree in the Electric Power Industry (MEPI)
- 3.2.2.4 Erasmus Mundus International Master in Economics and Management of Network Industries (EMIN)
- 3.2.2.5 Master in Railway Systems (MSF)
- 3.2.2.6 MBA in the Global Energy Industry
- 3.2.2.7 Master in Fire Protection Engineering (MIPCI)
- 3.2.2.8 Master's Degree in Smart Industry (MIC)
- 3.2.2.9 Master’s Degree in International Industrial Project Management
- 3.2.2.10 Master's Degree in Big Data Technologies and Advanced Analytics (MBD)
- 3.2.2.11 Master in Electricity Generation. Promotion, Technology and Operation (MGE) (On-line)
- 3.2.2.12 Master's Degree in Management Solutions (MCN)
- 3.2.2.13 Master in Project, Construction and Maintenance of High Voltage Electrical Transmission (On-line)
- 3.2.2.15 Official Master's Degree in Engineering Research (MIMSI)
- 3.2.2.16 Master's Degree in Smart Grids (MSG)
- 3.2.2.17 Master in Biomechanics Applied to Damage Assessment; Advanced Physical Therapy Techniques
- 3.2.2.18 Master in Mobility and Safety Engineering (MMS)
- 3.2.2.19 Master in Electrical Technology (MTE)
- 3.2.2.20 Official Master's Degree in Biomechanics and Sports Physiotherapy
- 3.2.2.21 Master in Environment and Energy Transition
- 3.3 Other Academic Activities
- 3.3.1 External Master Courses
- 3.3.2 Supervised Master Theses in other Universities
- 3.3.3 Developed Master Theses in other Universities
Chapter 4. Doctorate [ | ]- 4.1 ICAI Engineers' Association PhD Scholarship
- 4.2 Training complements
- 4.3 Training activities
| * | Subject title: | | | | Lecturer(s): | Javier García González | | | | |
- 4.4 Doctoral Theses
- 4.4.1 Comillas Submitted Theses
| * | Title: | Explainable Artificial Intelligence (XAI) techniques based on partial derivatives with applications to neural networks | | | Author: | Jaime Pizarroso Gonzalo | | | Supervisor(s): | Antonio Muñoz San Roque and José Portela González | | | Dissertation date: | Diciembre 15 , 2023 | | | | |
- 4.4.2 Submitted Theses in other Universities
- 4.4.3 Comillas Ongoing Theses
Chapter 5. Other Activities [ | ]- 5.1 EES-UETP
- 5.2 International Exchanges
- 5.3 Visiting professors
- 5.4 Visiting students
| * | Name: | David Cardona Vásquez | | | Department: | Institute of Electricity Economics and Energy Innovation. | | | University: | Graz University of Technology | | | City: | Graz | | | Country: | Austria | | | Period of stay: | May 2024 | | | Supervisor: | José Portela González | | | Otra información: | | | | | | | * | Name: | David Cardona Vásquez | | | Department: | Institute of Electricity Economics and Energy Innovation. | | | University: | Graz University of Technology | | | City: | Graz | | | Country: | | | | Period of stay: | May-July 2024 | | | Supervisor: | José Portela González | | | Otra información: | | | | | |
- 5.5 Courses offered and coordinated to external companies and institutions
| * | Lecturer(s): | José Portela González | | | Course title: | Training course: Using the NeuralSens package for interpretable machine learning on Neural Networks | | | Funding entity: | Universidad Complutense de Madrid | | | Date: | March-November 2023 | | | | | | * | Lecturer(s): | José Portela González, Jaime Pizarroso Gonzalo | | | Course title: | Advanced Machine Learning techniques course | | | Funding entity: | Xfera Móviles, S.A.U. | | | Date: | November-December 2023 | | | | | | * | Lecturer(s): | José Portela González | | | Course title: | AI Training Seminar Focused on Legal Sector Applications | | | Funding entity: | BLT LAW & TAX, S.L | | | Date: | November 2023 | | | | |
- 5.6 Conferences, Seminars, Roundtables and Workshops Contributions
- 5.7 Congress, Seminars and Journals Organization
- 5.8 Other Academic Activities Organization
- 5.9 Other Activities
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