OFFER DEADLINE30/09/2022 14:00 - Europe/Brussels
EU RESEARCH FRAMEWORK PROGRAMMEHE / MSCA
ORGANISATION/COMPANYInternational Research Projects Office
DEPARTMENTPromotion 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 Postdoctoral Fellowships (MSCA-PF) in 2022 at this University. Please note that applicants must comply with the Mobility Rule (more information about the 2022 call: http://sl.ugr.es/0cmA).
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 79 degrees, 188 master’s degrees and 28 doctoral programmes through its 127 departments and 22 centers. Consequently, the UGR offers one of the most extensive and diverse ranges of higher education programmes in Spain.
The UGR has been 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). The edition of the ARWU 2021 places the UGR in 201-300th position in the world and as the 2nd highest ranked University in Spain (http://sl.ugr.es/0cmF), reaffirming its position as an institution at the forefront of national and international research. The UGR stands out in the specialties of Library & Information Science (position 36); Food Science & Technology (39) and Hospitality & Tourism Management (51-75), according to the latest edition by specialties of this prestigious ranking (http://sl.ugr.es/0bSp). A little below in the ranking, the UGR stands out in Mathematics (76-100) and Mining & Mineral Engineering (76-100).
Additionally, the UGR counts with 7 researchers are at the top of the Highly Cited Researchers (HCR) list (http://sl.ugr.es/0cmD), most of them related to the Computer Science scientific area. It is also well recognized for its web presence (http://sl.ugr.es/0a6i), being positioned at 54th 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, 119 projects with total funding around 29.233 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.
Our understanding of the human brain comes primarily from in vivo imaging that has evolved from small samples to population studies. Most neurological and psychiatric disorders are now associated with cortical patterns of disease pathogenesis and progression, leading to increasing hope for high quality, reliable prognostic markers of treatment response that are the foundation of personalised medicine. Whole-brain analyses comprising a large number of statistical tests are frequently undertaken to ensure inclusion of relevant structures. These analyses have been traditionally conducted with classical statistics, either hypothesis testing or Bayesian inference, that relies on assumptions that are frequently violated. Consequentially, inflated type I error rates have become problematic, and a key contributor to the replication crisis. Technological advances are increasing spatial and temporal resolutions as well as the range of available measurements of anatomy and physiology; a true exemplar of the curse of dimensionality. In this context, analyses of contemporary large image repositories retain the difficulties associated with small sample sizes.
One promising solution is machine learning, where high-dimensional relationships between datasets are empirically established. Resampling and cross-validation in combination with statistical classifiers have been proposed for brain decoding. Beyond that, Statistical Learning Theory focuses on identifying discriminant functions for pattern recognition that optimise the robustness and complexity of the input-output relationship. Estimating dependencies, unlike in classical statistics, characterizes the actual relationships with a limited dataset. The project proposes a new view of deriving inferences from brain imaging data by applying SLT in a model-free, i.e. agnostic, methodology based on concentration inequalities that exploits an accurate and reliable data-driven algorithm: Statistical Agnostic Mapping.
Information Science and Engineeing (ENG)
For a correct evaluation of your candidature, please send the documents below to Professor Juan Manuel Górriz Sáez (email@example.com):
- Letter of recommendation (optional)
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