Expression of Interest

Contact Person/Scientist in Charge

  • Name and surname: María José del Jesus Díaz
  • Email:


Department / Institute / Centre

  • Name: UNIVERSIDAD DE JAÉN - Computer Science Department
  • Address: Campus Las Lagunillas s/n, 23071, Jaén (Spain)
  • Province: Jaén

Research Area

  • Information Science and Engineering (ENG)

Brief description of the institution:

The University of Jaén (UJA) is an EHEA medium-sized Spanish public university (some 16000 students and almost 1000 lecturers) that was established in 1993 and is organized into 6 main Schools/Faculties (Faculty of Law and Social Sciences, Faculty of Social Work, Faculty of Human Sciences, Faculty of Experimental Sciences, Faculty of Health Sciences and two Schools of Engineering). In addition to its 124 research groups, the University of Jaén is proud of its 4 Advanced Research Centers (Earth Sciences, Energy and Environmental Sciences, Olive Oil Research and Development, and Computational Sciences) and of its Institute on Iberian Archaeology Research. Moreover, UJA is integrated into a university network recognized as Campus of International Excellence in the fields of Agrifood (CEIA3), Climate Change (CamBio) and Historical Heritage (PatrimoniUN10).

UJA permanently welcomes new foreign research proposals as part of a conscious effort to increase its international profile and widen both its knowledge and its horizons.

Brief description of the Centre/Research Group (including URL if applicable):

SIMIDAT research group consists of Computer Science Department staff researching on the develop of intelligent systems for the extraction of useful knowledge from large data bases using fuzzy systems, neural networks, evolutionary systems and hybrid systems:

  • Predictive data mining (classification, regression, modelling, temporal series).
  • Descriptive data mining (pattern or trend detection, association rules, subgroup discovery, clustering).
  • Web mining.

SIMIDAT develops applications for medicine, economics, marketing, banking, agroindustry and the web, among other areas.

The research group is trained to face problems of extracting interesting and novel information in large databases using fuzzy systems, neural networks, evolutionary and hybrid systems. In Medicine, allows the extraction of information that relates patients’ symptoms with diagnoses, detection of outlier groups, or characterization of similar groups, exceptional groups or emerging patterns for medical study. In engineering, data mining methods are used to face modelling or optimization problems. In economics and finance, allows to characterize customers in order to study their suitability for certain financial products, or help in decision-making processes. Web mining methods allow to extract useful information to characterize customers browsing e-commerce sites or web pages, with the aim of structuring information in the most appropriate way to the user.


Project description:

Progress in devices for the generation and transmission of information makes data grow in volume and complexity. Therefore Data Science methods must advance towards new proposals in classification, time series prediction and subgroup description, towards developments for tasks as emerging pattern mining and exceptions, and also towards proposals that properly face more complex problems, such as data streaming, unbalanced data or multilabel data.

Computational intelligence techniques have been profusely applied in Data Science for representation and knowledge extraction or optimization: Fuzzy rule based systems, evolutionary learning or neural networks, to mention some examples of paradigms that allow the design of knowledge extraction algorithms, both independently and in combination. The problems addressed in Data Science, especially in real data analysis, usually represent a higher level of complexity. In these situations, Deep Learning is a technique with current factors that enhance its use in the development of new Data Science models: the development of hardware and software technologies for the distributed processing of information, the increase in the generation and storage of huge volumes of data, and the advances in information processing.

This project focuses in the development of Data Science models for complex problems including unbalanced classification, multilabel, data stream analysis and massive data, using Computational Intelligence techniques and including learning architectures from Deep Learning.

Special attention will be paid to the transfer of the methods developed, applying them to problems in areas such as biomedicine, biotechnology, renewable energy and business


CV, Motivation letter and Summary of project proposal (250 words) by 15th June 2018

I want to contact the Institution

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