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

International Research Projects Office

Hosting Information

Offer Deadline
EU Research Framework Programme


Organisation / Company
International Research Projects Office
Promotion and Advisory Unit
Is the Hosting related to staff position within a Research Infrastructure?

Contact Information

Organisation / Company Type
Higher Education Institute
Postal Code
Gran Vía de Colón, 48, 2nd floor


Professor David Morales Jimenez, 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 Fellowship (MSCA-PF) in 2022 at this University. Please note that applicants must comply with the Mobility Rule (for more information about the 2022 call, please consult:

Brief description of the institution:

The University of Granada (UGR) was founded in 1531 and is one of the largest and most important universities in Spain. With over 60,000 undergraduate and postgraduate students and 6,000 members of staff, the UGR offers over 70 undergraduate degrees, 100 master’s degrees (9 of which are international double degrees) and 28 doctoral programmes via its 127 departments and 22 centers. Accordingly, 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 institution’s commitment to continuously improving its human resource policies in line with the European Charter for Researchers and the Code of Conduct for the Recruitment of Researchers. The UGR is also internationally renowned for its excellence in diverse research fields and ranked among the top Spanish universities in a variety of ranking criteria, such as national R&D projects, fellowships awarded, publications, and international funding.

The UGR is one of the few Spanish Universities listed in the Shanghai Top 500 ranking - Academic Ranking of World Universities (ARWU). The 2021 edition of the ARWU places the UGR in 201-300th position in the world and as the second highest ranked university in Spain (, 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 of this prestigious ranking by specialties ( A little lower in the ranking, the UGR also stands out in Mathematics (76-100) and Mining & Mineral Engineering (76-100).

Additionally, the UGR has 7 researchers who are at the top of the Highly Cited Researchers (HCR) list (, most of these related to the area of Computer Science. It is also well recognized for its web presence (, being positioned at 54th place in the top 200 Universities in Europe.

Internationally, the University of Granada is firmly committed to its 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 a total funding of around 29.233 million euros.

Brief description of the Centre/Research Group:

The research group at UGR has extensive experience in data analysis and statistical signal processing, applied to computational biology and immunology. Using mathematical and computational approaches, the group’s research aims at developing new statistical inference methods and tools to extract knowledge from complex high-dimensional data (big data). A common factor is the use of advanced concepts from high-dimensional statistics and random matrix theory to develop machine learning (inference) solutions suited to the large-dimensional nature of contemporary biological data. The ultimate goal of the research is to enhance our understanding of complex biological systems; for example, deadly viruses such as HIV or SARS-CoV-2, which has caused the COVID-19 pandemic. Thus, the developed data analysis methods are applied to complex biological data (e.g., genetic sequences of viral proteins) in order to understand, for example, the evolutionary dynamics of deadly viruses causing infectious diseases; such understanding can ultimately help guiding the design of new vaccination or therapeutic strategies against them.

The group includes experts in multivariate analysis, biostatistics and machine learning; and their application to data analysis in real problems of computational biology/virology and vaccine design, including the processing of biological and immunological data. The group’s research activities are highly collaborative, involving a network of renowned experts from world-leading institutions. Active and dynamic collaborations are in place with the Signal Processing and Computational Biology Group (SPCB) at The Hong Kong University of Science and Technology (HKUST), with the Department of Statistics at Stanford University, with University of Melbourne and the Peter Doherty Institute for Infection and Immunity.

Project description:

Topics: Data Analytics, Machine Learning, Biostatistics, Computational Immunology.

In virology and immunology, there is a critical need to understand the biological and evolutionary behaviour of deadly viruses such as HIV, HCV and, more recently, SARS-CoV-2, which has caused the COVID-19 global pandemic; remarkably, no functional (effective) vaccine exists for any of these viruses. For HIV and HCV, one of the major challenges is that these viruses mutate and replicate at a high rate, and the resulting diversity enables them to escape the host immune response. The challenge is even more pronounced for the recent SARS-CoV-2, for which very little is yet known about its evolutionary behaviour. While huge efforts have been made to develop an effective vaccine for SARS-CoV-2 in order to stop its rapid spread, subsequent variants of the virus (e.g. Omicron variant) continue to wreak havoc and concerns have now turned towards future potential outbreaks caused by other mutant versions of the virus.

A key problem is that, while individual mutations in the viral genetic sequence may compromise viral fitness (i.e., the ability of the virus to replicate) immune escape is typically facilitated by other “compensatory” mutations that restore fitness. These compensatory networks of interactions within the viral genome are complicated and remain poorly understood. They do, however, leave co-evolutionary markers which may be inferred from measured correlations in sequence data.

An ideal vaccine would elicit antibodies that target parts of the viral proteins where mutations severely compromise the virus’ fitness. To guide such vaccine design, a systematic characterization of the fitness landscape, i.e., a mapping from the viral strain sequence to its fitness or viability, is required. Experimentally determining the complex fitness landscape is infeasible (due to the prohibitively large number of experiments), and computational approaches based on the statistical analysis of available viral sequence data are emerging as alternative strategies. A promising approach is to use unsupervised machine learning methods to infer a probability model from publicly-available viral sequences obtained from infected patients.

In this project, we will investigate and develop machine learning and statistical methods to infer probability models and fitness landscapes for HIV, HCV and SARS-CoV-2 protein sequences, which could ultimately lead to new vaccine strategies against these deadly viruses. The research will typically involve collaborations with renowned and internationally-leading researchers from Stanford University, University of Melbourne, and the Hong Kong University of Science and Technology; the applicant is expected to engage in these collaborative activities and to potentially visit some of these collaborators.

Research Area:

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

For a correct evaluation of your candidature, please send the documents below to Professor David Morales Jimenez (

  • CV
  • Letter of recommendation (optional)