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

Professor José Camacho Páez, from the Department of  Signal Theory, Networking and Communication at the University of Granada, welcomes postdoctoral candidates interested in applying for a Marie Skłodowska-Curie Postdoctoral Fellowship (MSCA-PF) in 2024 at this University. Please note that applicants must comply with the Mobility Rule (for more information about the 2024 call, please consult this link.

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 56,000 undergraduate and postgraduate students and more than 6,000 members of staff, the UGR offers over 90 undergraduate degrees, 164 master’s degrees (8 of which are international double degrees) and 28 doctoral programmes via its 124 departments and nearly 50 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 the institution’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. 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 2023 edition of the ARWU places the UGR in 201-300th position in the world and as the 1-2 highest ranked University in Spain (http://sl.ugr.es/0dwJ), reaffirming its position as an institution at the forefront of national and international research. From the perspective of specialist areas in the ARWU rankings (http://sl.ugr.es/0bSp), the UGR is outstanding in Food Science & Technology (ranked in the 48th position in the world), Hospitality & Tourism Management (ranked between 51th-75th position), and in the areas of Mathematics and Library & Information Science, both of them ranked between 76th-100th position. A little lower in the ranking, the UGR also stands out in the areas of Biomedical Engineering, Computer Science & Engineering and Nursing, in which the UGR is positioned at the rank in the 101-150th position. Finally, Dentistry & Oral Sciences is positioned between 151-200th position.

Additionally, the UGR counts with 9 researchers at the top of the Highly Cited Researchers (HCR) list, most of them related to the Computer Science scientific area. It is also well recognised for its web presence (http://sl.ugr.es/0a6i), being positioned at 76th 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 Framework Programme, Horizon 2020, the UGR obtained a total of 121 projects with a total funding of around €29,4 million. For the current Framework Programme, Horizon Europe, the UGR has obtained 74 projects, so far, with a total funding of almost €20 million.

Brief description of the Centre/Research Group:

The Computational Data Science (CoDaS) Lab is a research group created in 2022 the University of Granada. Our research interests include exploratory data analysis, machine learning and inferential statistics with multivariate techniques applied to data of very different nature: computational biology, analytical chemistry, communication networks, health, and ecology, among others. We specialize in extracting knowledge from complex data and the design of new algorithms and software tools to do so.

We provide:

• Techniques, software tools and computer servers for data analysis, design of experiments, sample size computation, interpretation of results, identification of biomarkers, estimation of statistical significance and other similar capabilities for the treatment of omics or other form of complex data.
• The ability to use state-of-the-art multivariate tools for the analysis of complex data: PCA/PLS-DA, sparse methods, variable selection, ASCA, PERMANOVA, Design of Experiments, etc.
• The ability to design new data analysis algorithms tailored to the analysis problem at hand for visualization, anomaly detection, classification, prediction, and optimization.
• The design of clinical/experimental studies, analysis of samples and interpretation of data.

Project description: 

The Project is centered on the study of statistical inference (statistical significance testing through p-values) for the detection of biomarkers in omics experiments with a focus on human health research. Statistical inference is ubiquitous in genomics and other -omics as a main tool for biomarker discovery. The state-of-the-art is based on the procedure to control the False Discovery Rate (FDR) by Benjamini and Hochberg, and the optimized version of the FDR by Storey and Tibshirani, often referred to as the q-value. The FDR and the q-value are suitable inference tools when large numbers of potential features (genes, SNPs, protein/metabolite abundances, etc.) are studied. Both methods are widely popular (> 90k and >10k references, respectively, according to Google Scholar) and are often referred to as main promoters of the widespread application of genomics in science. But both methods are intrinsically univariate, which reduces their statistical power.

A recent multivariate alternative to the FDR is Variable-selection ANOVA Simultaneous Component Analysis (VASCA). VASCA is a development of the CoDaS Lab, and we are working hard on its enhancement to deal with complex experimental designs, with repeated measures, random factors and other forms of complexities.

The selected candidate will have the freedom to select a specific project direction within the context introduced in the previous paragraphs. Some interesting lines are:

• Disentangling the advantages/disadvantages of methods based on the False Discovery Rate in comparison to multivariate methods (ASCA, PERMANOVA, VASCA).
• The analysis of single-cell experiments, meta-genomics or other form of complex -omics data.
• Developing and understanding the limitations of methods to compute the sample size (these methods are principal to derive the sample size in a clinical/experimental study, and as a result can have a huge impact in the derivation of significant results).

Research Area:

☒ Information Science and Engineering (ENG)

For a correct evaluation of your candidature, please send the documents below to Professor José Camacho Páez (josecamacho@ugr.es): 

• CV
• Letter of recommendation (optional)