Jacobo Ayensa Jimenez

Personal data

Education

  • BSc. Mathematics (Universidad Politécnica de Catalunya) | 2012.

  • MEng. Civil engineering, Numerical methods major (Universidad Politécnica de Catalunya) | 2013.

  • MEng. Mechanical Engineering (Universidad de Sevilla) | 2016.

  • PhD. Mechanical Engineering, extraordinary prize (Universidad de Zaragoza) | 2022.

  • BSc. Physics (UNED) (ongoing).

Scholarships and awards

  • Aragon government scholarship (2018 - 2022)

  • “Juan de la Cierva” grant (2025 - 2027), Ministerio de Ciencia, Innovación y Universidades. Ranked 2nd of the thematic area in Spain.

Academic and professional positions

  • Junior engineer, Abengoa Research (2013 - 2016).

  • Predoctoral researcher, Universidad de Zaragoza (2016 - 2017).

  • Data Scientist & Software developer, Instituto Tecnológico de Aragón (2017 - 2018).

  • Predoctoral researcher, Universidad de Zaragoza (2018 - 2022).

  • Postdoctoral researcher, Instituto de Investigación Sanitaria Aragón (2022 - 2025).

  • Postdoctoral researcher (Juan de la Cierva Program), Universidad Politécnica de Madrid (2025 - 2027).

Lecturing during 2025/26

  • Continuum mechanics (see moodle).

  • Solid mechanics (see moodle).

Research lines

Mathematical biology and computational methods in bioengineering, and more specifically:

  • Mathematical oncology: Development and calibration of mathematical and computational models for tumor evolution in in vitro experimental platforms.

  • Computational bioengineering: Design, analysis and implementation of finite element models for biomedical problems.

Scientific Machine Learning in Computational Engineering:

  • Data-driven Computational Mechanics: Design and implementation of data-driven methods.

  • Physics-Informed Machine Learning: Design, implementation and application to computational engineering of Physically-Guided Neural Networks with Internal Variables.

I also work on the application of Machine Learning state of the art techniques to specific healthcare problems, specifically medical image. Some examples in which I have worked before:

  • Lung cancer detection (RX).
  • Embryo selection and analysis (microscopy images and video).
  • Digital pathology for Prostate Cancer.

Other

Member of HKN - IEEE society, HKN - UNED Nu Alpha.

Associate member and collaborator of TME Lab @ Instituto de Investigación en Ingeniería de Aragón, TME Lab.

Selected publications

  • Muñoz-Sierra, R., Ayensa-Jiménez, J., & Doblaré, M. (2025). On the application of Physically-Guided Neural Networks with Internal Variables to Continuum Problems. Mechanics of Materials, 205, 105317. https://doi.org/10.1016/j.mechmat.2025.105317

  • Pérez-Aliacar, M., Ayensa-Jiménez, J., Ranđelović, T., Ochoa, I., & Doblaré, M. (2024). Modelling glioblastoma resistance to temozolomide. A mathematical model to simulate cellular adaptation in vitro. Computers in Biology and Medicine, 180, 108866. https://doi.org/10.1016/j.compbiomed.2024.108866

  • Ayensa-Jiménez, J., Doweidar, M. H., Sanz-Herrera, J. A., & Doblaré, M. (2021). Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers. Computer Methods in Applied Mechanics and Engineering, 381, 113816. https://doi.org/10.1016/j.cma.2021.113816

  • Ayensa-Jiménez, J., Pérez-Aliacar, M., Randelovic, T., Oliván, S., Fernández, L., Sanz-Herrera, J. A., … & Doblaré, M. (2020). Mathematical formulation and parametric analysis of in vitro cell models in microfluidic devices: application to different stages of glioblastoma evolution. Scientific Reports, 10(1), 21193. https://doi.org/10.1038/s41598-020-78215-3

  • Ayensa-Jiménez, J., Doweidar, M. H., Sanz-Herrera, J. A., & Doblaré, M. (2018). A new reliability-based data-driven approach for noisy experimental data with physical constraints. Computer Methods in Applied Mechanics and Engineering, 328, 752-774. https://doi.org/10.1016/j.cma.2017.08.027

See Google Scholar for a complete list of publications.

Active competitive projects