Marie Skłodowska-Curie Actions

MSCA-IF: Joint application at the University of Granada. Department of Signal theory, Networking and Communications

    01/09/2018 13:30 - Europe/Brussels
    H2020 / Marie Skłodowska-Curie Actions
    Spain, Granada
    International Research Projects Office
    Promotion and Advisory Unit

Professor Juan Manuel Górriz Sáez, from the Department of Signal theory, Networking and Communications at the University of Granada, welcomes postdoctoral candidates interested in applying for a Marie Skłodowska-Curie Individual Fellowships (MSCA-IF) in 2018 at this University. Please note that applicants must comply with the Mobility Rule (more information: http://sl.ugr.es/09Qg).

Brief description of the institution:

The University of Granada (UGR), founded in 1531, is one of the largest and most important universities in Spain. It serves more than 60000 students per year, including many foreign students, as UGR is the leader host institution in the Erasmus program. UGR, featuring 3650 professors and more than 2000 auxiliary personnel, offers a total of 75 degrees through its 112 departments and 28 centers.

UGR is also a leading institution in research, located in the top 5/10 of Spanish universities by a variety of ranking criteria, such as national R&D projects, fellowships awarded, publications, or international funding. UGR is one of the few Spanish Universities listed in the Shanghai Top 500 ranking (http://www.arwu.org/), and it is also well recognized for its web presence (http://www.4icu.org/top200/).

Internationally, we bet decidedly by our participation in the calls of H2020, both at partner and coordination. For the duration of the Seventh Framework Programme, the UGR has obtained a total of 66 projects, with total funding of 17.97 million euros, and for H2020, until 2015, more than 25 projects with total funding of more than 6 million euros. Our more than 3,000 researchers are grouped into 365 research groups covering all scientific fields and disciplines.

Brief description of the Centre/Research Group

The SIPBA group use computational and mathematical approaches based on the statistical learning theory to develop computer-aided diagnosis systems in the field of neuroscience. SiPBA aim to provide supporting tools to physicians in the early diagnosis of neuropathological diseases, such as Alzheimer or Parkinson diseases, that will influence treatment and patient management. In that sense, from the perspective of data analysis, SiPBA propose a number of statistical image analysis methods that compare an individual’s data with reference images of control subjects for the in-vivo assessment of brain functional/structural parameters in neurodegenerative diseases (for further information please visit: http://www.neuroscience.cam.ac.uk/directory/profile.php?gorriz

Project description

Nowadays, structural and functional imaging technologies in neuroscience are providing a high volume of information, where “big data”-based approaches are qualified to provide greater insight into psychiatric disorders, such as Autism Spectrum Condition (ASC). Beyond the standard data mining-based systems, and given the massive amounts of computational power available, researchers are in the starting point to overtake the classical psychiatric paradigms of the last fifty years, proposing new measures of variable importance and deep learning architectures to reveal hidden features in the aforementioned disorders. 

Recently, as a part of the machine learning theory, the deep learning (DL) framework has been successfully applied to the vast field of engineering applications, such as computer vision or speech recognition systems. These preprocessing methods are devoted to enhance the quality of the extracted features in order to boost the performance of the decision systems at the final stage. Nevertheless, the clustering problem in diagnostics constitutes a challenging task in some neuroimaging applications, due to the heterogeneity of the condition-specific pattern, which is widespread across the scans from several imaging techniques. 

The contributions of the project are two-fold: i) to validate the theories of Autism by means of recent advances in machine learning-based models, from linear and low-dimensional schemes to the deep neural network architectures, and voxel-based morphometry, and ii) to extend these innovative models to the characterization of the well-known neuro-pathological patterns, such as the ones presented in Alzheimer and Parkinson diseases (AD, PD). 

Research Area

  • Information Science and Engineering (ENG) 
  • Life Sciences (LIFE) 

For a correct evaluation of your candidature, please send the documents below to Professor Juan Manuel Górriz Sáez (gorriz@ugr.es):

  • CV
  • Letter of recommendation (optional)