04/05/2021
Marie Skłodowska-Curie Actions

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

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  • OFFER DEADLINE
    01/09/2021 15:00 - Europe/Brussels
  • EU RESEARCH FRAMEWORK PROGRAMME
    HE / MSCA
  • LOCATION
    Spain, Granada
  • ORGANISATION/COMPANY
    International Research Projects Office
  • DEPARTMENT
    Promotion and Advisory Unit
  • LABORATORY
    NA

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 Postdoctoral Fellowships (MSCA-PF) in 2021 at this University. Please note that applicants must comply with the MobilityJulio SantiagoJulio SantiagoRule (more information about the 2020 call: http://sl.ugr.es/0aNV, the 2021 call is not yet open).

Brief description of the institution:

The University of Granada (UGR), founded in 1531, is one of the largest and most important universities in Spain. With over 60.000 undergraduate and postgraduate students and 6.000 staff. UGR offers a total of 89 degrees, 110 master’s degrees and 28 doctoral programmes through its 123 departments and 27 centers. Consequently, the UGR offers one of the most extensive and diverse ranges of higher education programmes in Spain.

The UGR has awarded with the "Human Resources Excellence in Research (HRS4R)", which reflects the UGR’s commitment to continuously improve its human resource policies in line with the European Charter for Researchers and the Code of Conduct for the Recruitment of Researchers. UGR is also a leading institution in research, located in the top 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 - Academic Ranking of World Universities (ARWU) (http://sl.ugr.es/0bsW). The UGR is amongst the 201-300 first universities of the world, between 2nd-5th position of Spanish universities and number 1 in the Andalusian Region in the Shanghai Top 500 ranking. Specialties at UGR that stand out are Library & Information Science (position 32) and Food Science & Technology (position 36). Moreover, the UGR is also situated amongst the first 100 universities in Mining & Mineral Engineering between (76th-100th position), in Mathematics (between 76th-100th position) and in Hospitality & Tourism Management (between 76th-100th position). The edition of the ARWU places the UGR in 201-300th position in the world and as the 4th highest ranked University in Spain, reaffirming its position as an institution at the forefront of national and international research.

Additionally, the UGR has 8 researchers at the top of the Highly Cited Researchers (HCR) list in Computer Sciences & Engineering (position 101-150). It is also well recognized for its web presence (http://sl.ugr.es/0a6i), being positioned at 43th place in the top 200 Universities in Europe.

Internationally, we bet decidedly by our participation in the calls of the Framework Programme of the European Union. For the duration of the last two Framework Programmes, the UGR has obtained a total of 67 projects, with total funding of 18.029 million euros, and for H2020, 118 projects with total funding around 29.115 million euros.

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.

Project description:

Nowadays, structural and functional imaging technologies in neuroscience are providing a high volume of information, where machine learning-based approaches are qualified to provide greater insight into psychiatric disorders, such as the 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 several neurological 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. DL methods combine the power of automatic feature learning algorithms and boosted neural-based decision systems at the final stage. Nevertheless, the clustering or spectrum problem in diagnostics constitutes a challenging task in some neuroimaging applications, due to the heterogeneity of the condition-specific patterns, which are widespread across the scans acquired from several imaging techniques.

The contributions of the Deep-Neuromaps project are two-fold: i) to validate the theories of Autism by means of recent advances in machine learning applied to voxel-based morphometry, from linear and low-dimensional schemes to deep neural network architectures, and ii) to extend/extrapolate these innovative models utilizing transfer learning strategies 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)

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)

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