Abstract:

Scientists have been studying the heart and the brain for centuries but mainly at the individual organ level. There is a need for a more systemic approach to study some neurological and cardiovascular processes that remain not well understood. A good example is Alzheimer’s disease (AD), one of the most important healthcare challenges of our society. There is strong evidence on the relevance blood flow anomalies in brain aging, but they have rarely been studied together with a systemic approach based on mechanistic models. 

The aim of this project is to create a computational platform, linking heart and brain systems, to test the influence of the cardiovascular system on brain aging in normal and abnormal conditions. It will include data science (e.g., artificial intelligence) techniques, and biophysical multi-scale models to identify different trajectories, including cardiovascular insults. Advanced visual analytics tools will also be developed for scientific dissemination and promote healthy aging life. 

Large clinical datasets of hearts and brains from controls and patients will be available thanks to the excellent network of scientific collaborators involved in the project. It includes researchers from the Fundació Pasqual Maragall (the largest centre in Barcelona on AD research), the Auckland Bioengineering Institute (New Zealand), and the National Institute for Research in Digital Science and Technology (Inria, France), among others. Previous joint work between some of these institutions have resulted in pioneering scientific outcomes to studying heart-brain relationships (https://hal.inria.fr/tel-03242796). 

Open position for a predoctoral researcher:

This position includes a teaching commitment load of 45 hours per academic year.

Topic: Computational tools to analyse the influence of the cardiovascular system in neurodegenerative diseases (Aging-Heart@Brain)

Requirements: 

We are looking for a highly motivated young researcher with a master’s degree in biomedical engineering, Physics, Mechanical Engineering, Applied Mathematics, Computational Science, or related disciplines. Having proven programming skills would be of importance. Candidates must have excellent teamwork and communication skills (fluency in English is a must). Admission in the PhD program of the Department of Information and Communication Technologies at UPF is a prerequisite to enjoy the contract.

Starting date (planned): Academic course 2023-24

Application deadline: June 30th 

Gross yearly salary: 

Gross monthly salary: 1680€. (To increase to 2020€ during the fourth year of the PhD).

This position is co-funded by the PhD fellowship program of the Department of Information and Communication Technologies at Universitat Pompeu Fabra (DTIC-UPF), and the María de Maeztu Strategic Research Programme at DTIC-UPF on Artificial and Natural Intelligence for ICT and beyond. Its benefits and conditions are available at: https://www.upf.edu/web/etic/predoctoral-research-contracts. 

The PhySense group and the BCN-MedTech unit form an ideal environment for training early-stage researchers, as has been confirmed during the last years by Spanish and European agencies. All PhD students have large office spaces and their own desk with networked computers, appropriate software, access to state-of-the-art equipment, excellent library and information services, language and scientific communication training and cultural/sport services. Our PhD students usually get maximum thesis qualifications, with publications in high impact journals. We provide interdisciplinary doctoral training aimed at educating academic persons with the required skills and flexibility to function and perform in a broad range of employment opportunities in a clinical, academic, or industrial environment, in the field of Biomedicine and Bioengineering. 

The PhD candidate will be surrounded by a large team of senior and junior researchers from different disciplines in the hosting group (PhySense, BCN-MedTech research unit at UPF). PhySense’s research is focused on data science and modelling methods in a wide variety of medical fields, with emphasis on the clinical translation. This project will be complementary to existing research in our institution. For instance, to study haemodynamics and morphology in atrial fibrillation in relation with stroke, which is another interesting heart-brain axis application.

For application or further information, contact

Oscar Camara ([email protected]) – PhySense (https://www.upf.edu/web/physense) – BCN MedTech (https://www.upf.edu/web/bcn-medtech)

More information about the María de Maeztu Strategic Research Programme at DTIC-UPF on Artificial and Natural Intelligence for ICT and beyond: https://www.upf.edu/web/mdm-dtic . 

This program receives funding from MCIN/AEI /10.13039/501100011033 under the Maria de Maeztu Units of Excellence Programme (CEX2021-001195-M)