Aitor Goti

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Profesor

Universidad de Deusto

Spain

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About

Aitor Goti is a Profesor at Universidad de Deusto, Spain. His research focuses on the application of advanced statistical methods and machine learning techniques in areas such as metallurgical copper recovery prediction, thermal comfort improvement in schools, and geostatistical estimation. Recent articles by Professor Goti emphasize the use of trivariate copula-based approaches and the development of transdisciplinary frameworks for sustainability and resource estimation.

Recent Grants

Grant: Close

learning outcomes of qualifications to ESCO skills

Open Date: 2022-01-31

Close Date: 2022-12-31

Grant: Close

EQVEGAN - European qualifications & competences for the Vegan food Industry

Open Date: 2020-12-01

Close Date: 2022-12-31

Grant: Close

ALLVET: HE and VET alliance establishment according to Bologna Principals implementation via VET teachers' capacity building

Open Date: 2020-01-15

Close Date: 2022-12-31

Grant: Close

Skills Alliance for Industrial Symbiosis - a Cross-sectoral Blueprint for a Sustainable Process Industry (Project number 400612429, call EAC-A03-2018)

Open Date: 2020-01-01

Close Date: 2022-12-31

Grant: Close

University Business Cooperation for Promoting Virtual, Augmented and Mixed Reality Applications within Small and Medium-sized Manufacturing Companies

Open Date: 2020-01-01

Close Date: 2021-12-31

Articles (20)

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Professor

University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU)

SPAIN

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Aitor Goti

Research Interests

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About

Recent Grants

Articles (20)

Comparison of Trivariate Copula-Based Conditional Quantile Regression Versus Machine Learning Methods for Estimating Copper Recovery

Comparison of Trivariate Copula-Based Conditional Quantile Regression Versus Machine Learning Methods for Estimating Copper Recovery

Development of a Transdisciplinary Research-Based Framework for the Improvement of Thermal Comfort of Schools Through the Analysis of Shading System

BUILDING SUSTAINABILITY: MULTIDISCIPLINARY CONTRIBUTION FROM THE UNIVERSITY

Metallurgical Copper Recovery Prediction Using Conditional Quantile Regression Based on a Copula Model

Metallurgical Copper Recovery Prediction Using Conditional Quantile Regression Based on a Copula Model

Development of a Transdisciplinary Research-Based Framework for the Improvement of Thermal Comfort of Schools Through the Analysis of Shading System

MODELING MUNICIPAL WASTE COLLECTION USING ARTIFICIAL INTELLIGENCE TECHNIQUES

Desarrollo de métodos basados en redes neuronales en la estimación de recursos minerales

Application of the k-Prototype Clustering Approach for the Definition of Geostatistical Estimation Domains

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Identifying Future Skill Requirements of the Job Profiles for a Sustainable European Manufacturing Industry 4.0

Development and Application of a Multi-Objective Tool for Thermal Design of Heat Exchangers Using Neural Networks

Mechanical Behavior Modeling of Containers and Octabins Made of Corrugated Cardboard Subjected to Vertical Stacking Loads

Skills Requirements for the European Machine Tool Sector Emerging from Its Digitalization

Validation of Real Case Solving (RCS) Methodology as an Efficient Engineering Learning Tool

Skills Needs of the Civil Engineering Sector in the European Union Countries: Current Situation and Future Trends

Analysis of the Air Quality of the Basque Autonomous Community Using Spatial Interpolation

A Guide for the Food Industry to Meet the Future Skills Requirements Emerging with Industry 4.0

Modeling the Municipal Waste Collection Using Genetic Algorithms

Collaborators (7)

Xabat Oregi

Amaia Santamaría

Aitor Oyarbide-Zubillaga

Diego López-de-Ipiña

Alberto de la Calle

Roberto Carballedo

IRANTZU ALVAREZ GONZALEZ

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