Job offer

The successful candidate will work within the framework of the SMART-CM project, as part of a research training programme in the field of cardiovascular computational modelling. Under the supervision of the project's researchers, they will carry out the following tasks:

• Development of skills in the processing and analysis of cardiac medical imaging (CT, MRI and echocardiography), including segmentation, geometric reconstruction and quantitative analysis.

• Implementation and validation of computational tools for the analysis of intracardiac flow and the estimation of haemodynamic biomarkers related to thromboembolic risk.

• Participation in the development of computational fluid dynamics (CFD)-based numerical models applied to patient-specific cardiac geometries.

• Application of machine learning and artificial intelligence techniques to improve data processing and the integration of multimodal information.

• Collaboration in the integration of flow and coagulation models within advanced computational environments.

• Support in the management and analysis of clinical and imaging databases, in coordination with the project's clinical team.

• Participation in the implementation and use of high-performance computing tools, including the execution of simulations on computing clusters.

• Documentation of the work carried out and involvement in the drafting of scientific articles and conference papers.

• Participation in training activities within the PhD programme and in project coordination meetings.

Bachelor's degree in Mathematics, Physics or Engineering, Bachelor Degree or equivalent, The candidate must have a solid background in computer science as applied to scientific research. Knowledge of scientific programming, particularly in Python and/or MATLAB, is highly valued, as is experience with Linux environments and the use of version control systems (Git). Familiarity with handling and processing large volumes of data, complex data structures, and workflow automation is desirable. Experience in medical image processing, including segmentation, registration, and quantitative analysis of images (CT, MRI, or echocardiography), as well as the use of common scientific libraries (NumPy, SciPy, scikit-image, SimpleITK, etc.), will be given special consideration. Knowledge of machine learning and artificial intelligence techniques is a plus. Previous experience in code parallelization, the use of computing clusters and/or GPUs, as well as basic knowledge of computational fluid dynamics or numerical simulation, will also be valued. The ability to integrate computational tools with real clinical data, properly document code, and work in multidisciplinary teams will be a key requirement. A proactive attitude toward learning new technologies and adapting to highly innovative research environments is also expected.

Languages ENGLISH