Modelling Tumour Dynamics and Radiotherapy Response: Towards Sustainable and Energy Efficient Cancer Treatments

Solid tumors often undergo macroscopic growth arrest, a phenomenon that remains
quantitatively unresolved despite extensive modelling efforts. This Ph.D. project aims to advance a renormalisation-group (RG) field-theoretical framework that captures the universal features of tumor arrest from first principles, with the goal of supporting more predictive and sustainable cancer treatment strategies.

The objectives of this Ph.D. thesis are twofold:

• to further develop and refine the RG-based model to enhance its biological
  interpretability and robustness across different tumor types;
• to extend the model to simulate and predict solid tumor response to innovative
  radiotherapy modalities such as spatially fractionated radiotherapy (SFRT) and FLASH therapy.

These emerging techniques, which show promise in increasing normal tissue sparing while maintaining tumor control, introduce spatiotemporal inhomogeneities in dose delivery. Such features challenge conventional models but may be naturally incorporated into the RG formalism via spatially varying couplings or external perturbations.

The candidate will explore how radiotherapy alters critical exponents, scaling behavior, and fixed-point structure within the model. Computational modelling and simulations will be key

This project has received funding from the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101216811. to validating theoretical predictions and identifying universal biomarkers of therapeutic efficacy.

Through a combination of theoretical physics, computational modelling, and interdisciplinary research, the candidate will contribute to advancing both our understanding of tumor dynamics and the development of sustainable, energy-aware technologies in modern radiotherapy.

The ideal candidate should hold a degree in Physics and a Master’s in the Applications of Physics to Medicine or a closely related field. A strong background in programming (e.g., Python, or similar languages) is essential, as the project involves computational analysis and data-driven modelling. Previous experience in the modelling of biological systems is highly desirable. We are seeking a candidate with a genuine interest in multidisciplinary research, particularly at the intersection of physics, biology, and medicine. The position requires excellent problem-solving skills, the ability to work independently and collaboratively within a research team, and a high level of proficiency in English, both written and spoken. Enthusiasm for learning and contributing to cutting-edge research is also essential.

IONHE (2021 SGR 00607) is an interuniversity research group whose expertise covers a wide range of topics in the fields of Ionizing Radiation, Health and Environment. Regarding Health, the activities are related with the measurement and modeling of ionizing radiations exposure in different medical applications (radiotherapy, RX imaging, nuclear medicine). Concerning the environment, the group activities are related with the application of airborne radiation detectors carried by unmanned aerial systems for mapping radioactivity and source localization, modeling of radon levels and their progeny in closed buildings, tracers applications for climate and atmospheric processes.

SGR 00649 focuses on Particle Physics: Standard Model (SM), Beyond the Standard Model, and Astroparticles and Cosmology. We focus on improving the determination of fundamental SM parameters based on Effective Field Theories and Mathematical Models. This all ows us to explore anomalies in the flavor sector, using as well as amplitude methods. We also contribute to understanding the role of particle physics in the early universe with data related to gravitational waves and dark matter.