Your brain has its own waterscape: whether you are reading or sleeping, fluid flows through the brain tissue and clears waste in the process. These physiological processes are crucial for the well-being of the brain. In spite of their importance we understand them but little. Mathematics and numerics could play a crucial role in gaining new insight. Indeed, medical doctors express an urgent need for multiscale modeling of water transport through the brain, to overcome limitations in traditional techniques. Surprisingly little attention has been paid to the numerics of the brain's waterscape however, and fundamental knowledge is missing. In response, the Waterscales ambition is to establish the mathematical and computational foundations for predictively modeling fluid flow and solute transport through the brain across scales -- from the cellular to the organ level. The project aims to bridge multiscale fluid mechanics and cellular electrophysiology to pioneer new families of mathematical models that couple macroscale, mesoscale and microscale flow with glial cell dynamics. For these models, we will design numerical discretizations that preserve key properties and that allow for whole organ simulations. To evaluate predictability, we will develop a new computational platform for model adaptivity and calibration. The project is multidisciplinary combining mathematics, mechanics, scientific computing, and physiology. If successful, this project enables the first in silico studies of the brain's waterscape across scales. The new models would open up a new research field within computational neuroscience with ample opportunities for further mathematical and more applied study. The processes at hand are associated with neurodegenerative diseases e.g. dementia and with brain swelling caused by e.g. stroke. The Waterscales project will provide the field with a sorely needed, new avenue of investigation to understand these conditions, with tremendous long-term impact.

Scientific breakthroughs in neuroscience explain the need for sleep and the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases in terms of fluid dynamics: the waste created during the day is cleared away as we sleep or it accumulates. However, a decade of research after the original theory was posed has revealed that the underlying physical mechanisms are still not understood and that the advanced mathematical tools are needed. In this project we propose a research program addressing 1) new fluid dynamics mechanisms, 2) novel numerical analysis for advanced multi-physics brain simulations, and 3) a framework for patient-specific simulations. We will exploit reduced order and machine learning methods in addition to finite element solutions. With successful delivery, the "aCleanBrain" will establish the main drivers of brain fluid dynamics, provide a foundation for accurate, efficient and robust algorithms for multi-physics problems and develop a software framework for advanced biomechanical brain simulations.

Large language models (LLMs) have gained widespread attention and user adoption. These models, when trained on source code from platforms like GitHub, acquire a deep understanding of both the semantic and syntactic structures of code (i.e., code language models or CLMs). This understanding has paved the way for significant advancements in software engineering, offering developers valuable assistance in labor-intensive tasks like bug fixing and code writing. While CLMs offer tremendous assistance in software engineering tasks, their massive data requirements result in substantial energy consumption and CO2 emissions. This proposal challenges the conventional wisdom that "more data is better" and instead advocates for a refined approach to data in the training of CLMs. We propose that by intentionally decreasing training data volume while simultaneously enhancing data quality through data refinement techniques, we can reduce energy consumption while maintaining or even improving performance on software engineering tasks. The condenSE project represents a pioneering effort to advance sustainable training practices for CLMs. Unlike existing methods, which are often non-systematic or limited to natural languages, condenSE promises a comprehensive approach to achieve sustainability via data refinement for CLMs. This initiative is well-aligned with the EU Green Deal initiative and UN Sustainable Development Goals, and the increasing attention for LLMs and CLMs means that now is the right time to address their sustainability. The proposal's potential for success is further strengthened by the host institution's international standing, providing a wide range of collaborative opportunities, as well as by the complementary expertise of the applicant and supervisor, spanning the fields of software engineering, machine learning, dataset creation, and language model application.