Hosting Information
Offer Deadline: Wed, 9 Sep 2026 - 16:00
EU Research Framework Programme: Horizon Europe - MSCA
Country: Spain
City: Granada
Organisation/Institute
Organisation / Company: University of Granada
Department: Statistics and Operation Research
Laboratory: Visual Information Processing Group
Is the Hosting related to staff position within a Research Infrastructure? No
Contact Information
Organisation / Company Type: Higher Education Institution
Website: http://www.ofpi.ugr.es; https://scholar.google.es/citations?user=9gO0JeEAAAAJ&hl=es; https://ccia.ugr.es/vip/index.php
Email: promofpi@ugr.es; pablomorales@ugr.es
State/Province: Granada
Postal Code: 18071
Street: Gran Vía de Colón, 48, 2nd floor
Description
MSCA-PF: Joint application at the University of Granada. Department of Statistics and Operation Research.
Professor Pablo Morales Álvarez, from the Department of Statistics and Operation Research at the University of Granada, welcomes postdoctoral candidates interested in applying for a Marie Skłodowska-Curie Postdoctoral Fellowship (MSCA-PF) in 2026 at this University. Please note that applicants must comply with the Mobility Rule (for more information about the 2026 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 more than 57,000 undergraduate and postgraduate students and almost 7,000 members of staff, the UGR offers 97 undergraduate degrees, 157 master’s degrees (7 of which are 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 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 2025 edition of the ARWU places the UGR in 301-400th position in the world and as the 3-8 highest ranked University in Spain, reaffirming its position as an institution at the forefront of national and international research. From the perspective of specialist areas in the ARWU rankings, the UGR is outstanding in Mathematics, Artificial Intelligence and Dentistry & Oral Sciences (ranked between 51th-75th position), Computer Science & Engineering, Education and Hospitality & Tourism Management (between 76-100th position), and in the areas of Food Science & Technology and Business Administration (between 101-150th position). A little lower in the ranking, the UGR also stands out in the areas of Earth Sciences, Law, Management, Nursing, Psychology and Statistics, in which the UGR is positioned in the 151-200th position.
Additionally, the UGR counts with 4 researchers at the top of the Highly Cited Researchers (HCR) list, most of them related to the Computer Science and Mathematics scientific areas. It is also well recognised for its presence in the top 200 Universities in Europe at 83th place.
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 prevoius Framework Programme, Horizon 2020, the UGR obtained a total of 124 projects with a total funding of more than €30 million. For the current Framework Programme, Horizon Europe, the UGR has obtained 136 projects, so far, with a total funding of more than €38 million.
Brief description of the Centre/Research Group:
The Visual Information Processing Group (VIPG), founded in 2002 at the University of Granada (UGR), is a consolidated research unit specializing in machine learning, probabilistic modeling, and advanced image analysis. The group maintains a strong international profile through stable collaborations with world-class institutions, including Northwestern University (USA) and the University of Cambridge (UK). Their scientific impact is evidenced by recent high-impact publications in top-tier venues such as NeurIPS, ICLR, ICCV and MICCAI, alongside prestigious national and international recognitions (e.g., Meta best student paper award at ICIP 2025, 2023 BBVA-SEIO national award for applied statistics, 2022 SCIE-BBVA Award for promising young computer scientists).
A primary focus of the VIPG is the application of its expertise to digital pathology, aiming to leverage AI for cancer diagnosis and prognosis. Since 2017, the group has led several major research projects, such as SICAP, AI4SkIN, and ASSIST, which focus on the analysis of prostate and skin tumors. Their work in this field involves developing probabilistic deep learning models capable of handling data scarcity, quantifying diagnostic uncertainty, and improving system interpretability. Furthermore, the group has contributed significant open-source tools to the community, most notably TorchMIL, a PyTorch-based library specifically designed for deep Multiple Instance Learning in histopathological image analysis.
See recent publications and projects at the research group website: https://ccia.ugr.es/vip/index.php
Project description:
Reliable and Generative Machine Learning for Digital Pathology
Keywords:
- Probabilistic Machine Learning
- Generative Modeling (Diffusion/Flow Matching)
- Digital Pathology
- Uncertainty Quantification & Interpretability
- Foundation Models
This research line focuses on developing advanced probabilistic machine learning frameworks to bridge the gap between AI research and clinical practice in digital pathology. The core objective is to move beyond deterministic "black-box" models by integrating uncertainty quantification and interpretability (UQ&I) into deep learning architectures, ensuring that AI-assisted diagnoses are both accurate and trustworthy for clinicians.
A major pillar of this line involves generative modeling, leveraging state-of-the-art techniques such as Diffusion Models, Flow Matching, and Variational Autoencoders (VAEs). These models are applied to critical preprocessing tasks like robust stain normalization and artifact restoration to mitigate variability across different scanners and laboratories. Furthermore, generative models are used for Generative Manifold Alignment, learning the specific distribution of local clinical data to enhance Out-of-Distribution (OOD) detection.
The research extensively leverages Foundation Models, adapting massive, pathology-specific pre-trained models to downstream diagnostic tasks through efficient fine-tuning and probabilistic adapters. Using Multiple Instance Learning (MIL) and global whole-slide image (WSI) analysis, the group applies these methods to real-world clinical challenges, particularly in the diagnosis and prognosis of prostate and skin cancer. This multidisciplinary effort involves close collaboration with expert pathologists to ensure that the developed algorithms meet rigorous clinical specifications and are validated under real-world conditions.
Research Area:
- Information Science and Engineering (ENG)
- Life Sciences (LIFE)
For a correct evaluation of your candidature, please send the documents below to Professor Pablo Morales Álvarez (pablomorales@ugr.es):
- CV
- Letter of recommendation (optional)
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