PhD position for Advance data analytics and visualization applied to craniofacial dysmorphology patterns

We are looking for a highly motivated PhD candidate to develop new machine learning and visual analytics methods for the exploration, analysis, and explainability ofmultimodal data in the context of craniofacial dysmorphology. This position involves investigating interdisciplinary aspects such as computer vision, medical image analysis, data science, and interactive visualisation.

It is estimated that 30% to 40% of genetic disorders produce alterations in the normal morphology of the face and the head, which can impact swallowing, breathing, hearing, vision, speech, and -more importantly- cognitive development. Thus, craniofacial anomalies have been highlighted as an index of developmental disturbance at the early stages of life. Initial diagnosis is often based on visual inspection from paediatricians but, unfortunately, dysmorphology is hard to identify in this way, and massive genetic screening is expensive and impractical. For these reasons, there is a growing interest in using facial imaging as a low-cost tool for genetic pre-screening, i.e., to highlight suspicious cases for further study. In this context, our on-going project eSCANFace is developing the technology necessary to make such early screening more accurate, more accessible, and more comprehensive, and to allow its deployment as early as possible in life. We are developing a facial analysis framework that leverages deep-learning technology and computer vision for face dysmorphology assessment in babies (including fetuses) to achieve better syndrome prioritization and diagnosis. The project is coordinated by two DTIC research groups (BCN-MedTech and IMVA) and counts with the support and collaboration of 5 other national and international institutions.

As part of this project, the PhD candidate will develop novel techniques to integrate and analyse clinical and facial imaging data, with the aim to identify and display facial phenotypes of genetic disorders.

This position is co-funded by the PhD fellowship program of the the Department of Information and Communication Technologies at Universitat Pompeu Fabra (DTIC-UPF), and the María de Maeztu Strategic Research Program at DTIC-UPF on Artificial and Natural Intelligence for ICT and beyond.

• More information about the DTIC-UPF PhD fellowship program https://www.upf.edu/web/etic/predoctoral-research-contracts
• More information about the María de Maeztu Strategic Research Program at DTIC-UPF on Artificial and Natural Intelligence for ICT and beyond:https://www.upf.edu/web/mdm-dtic

For application, send CV to Gemma Piella, BCN MedTech, gemma.piella@upf.edu Federico Sukno, IMVA, federico.sukno@upf.edu