O-Health is a Consolidator grant from the European Research Council, awarded by the European Commission under the HORIZON Action. It is led by Prof. Jérôme Noailly, from the BCN MedTech research unit of the Department of Information and Communication Technologies (DTIC), at the Universitat Pompeu Fabra (UPF), Barcelona, Spain.
Non-communicable diseases (NCD) that involve load-bearing organs emerge silently according to complex mechanisms that are likely to involve inter-disease systemic communications. Clinical explorations cannot apprehend such intricate emergence, O-Health postulates that multiscale in silico models can. Digital twin technologies for health have progressed a lot in the last decades, but multi-disease transversal modelling at the body scale is yet to be developed. It requires to couple small to large-scale model components with appropriate balance of phenomenological and mechanistic approaches, to cope with overwhelming biological complexity, preserve interpretability and incorporate real-world data. This is the niche of O-Health that proposes a scalable ecosystem of multiscale NCD models interrelated through a body-wide systemic model of low-grade inflammation. The project tackles such vertical and transversal physiology-based computational modelling through four major NCD, lung emphysema, atherosclerosis, intervertebral disc degeneration and knee osteoarthritis that affect load-bearing organs at different anatomical locations. Each NCD model will vertically share predicted variables with an interface model of endothelial cell dysfunction that communicates with a transversal model of body-wide systemic communications. The O-health ecosystem shall be modular and interoperable. Models and simulations will combine finite element models at the organ /tissue scales and systems biology models at lower scales, with machine learning models for the incorporation of real-world data out of population cohorts. Interoperability shall be ensured through declarative standard languages such as Field and Systems Biology Mark-up Languages.
Description of the position
The successful Candidate will be a senior postdoctoral researcher, with proven experience in data science and machine learning. He/She will be based at DTIC, UPF and will be responsible for the management and mining of health data collected for the O-Health project, out of existing patient and population cohorts. Scientific and technical competencies are necessary in interpretable machine learning, nonlinear unsupervised learning, as well as in federated and transfer learning. Advanced programming skills and experience in handling health and/or biomedical data are required. Knowledge of data management plans will be valued. The Researcher will become progressively responsible for the data science operations of the BMMB, to properly mine real-world health data and couple the latter to the physics- and biology-based models, developed by the other researchers of the group for interpretable predictive modelling. While a high level of independence is expected, (s)he shall report periodically to PI, to ensure (i) that the timelines of the project and deliverables are met, and (ii) that the project results can be properly disseminated through scientific and medical congresses and journals. As such, communication, teamwork, and leadership skills will be chiefly important.