SWAG合集

The project falls within the field of Modelling and Simulation (M&S), with a focus on biomechanics, biofluid dynamics, and machine learning applications in healthcare. Specifically, it addresses the simulation of cerebral blood flow and brain tissue mechanics to improve stroke treatment. Stroke is a leading cause of death and disability worldwide, making advancements in its diagnosis and treatment highly relevant. Computational models offer a powerful means to understand stroke mechanisms, predict treatment outcomes, and personalize patient care. By integrating numerical techniques like the finite element method and machine learning, this research contributes to the growing field of digital healthcare, which aims to enhance clinical decision-making and improve patient outcomes.

The primary focus of the project is to develop and refine simulation tools and machine learning solutions to advance stroke treatment. This involves improving existing computational models that simulate cerebral blood flow, oxygen distribution, and brain tissue deformation during a stroke. The project builds on previous research from the INSIST initiative and contributes to the EU-funded GEMINI project, which seeks to enhance stroke treatment strategies through advanced modelling and simulation. A key objective is to validate and optimize poroelastic finite element models of brain tissue, making them more accurate and clinically relevant. Additionally, machine learning techniques may be integrated to accelerate simulations and improve medical image processing, ultimately aiding in stroke diagnosis and treatment planning.

Please note that this is a self-funded project and that there is no tuition-fee or maintenance bursary attached to this position.

The student will benefit from access to local resources, including Cranfield-based and national computing facilities, such as CRESCENT2 and ARCHER2.

The expected impact of this research project is twofold: advancing the understanding of stroke mechanisms and improving clinical applications for stroke treatment. By refining simulation tools that accurately model cerebral blood flow and brain tissue behaviour, the project aims to provide deeper insights into stroke pathophysiology, aiding in early detection and better patient-specific treatment planning. The integration of machine learning techniques is anticipated to accelerate the computational processes, enabling faster and more precise simulations that could significantly enhance clinical decision-making. Additionally, the project’s collaboration with the GEMINI initiative ensures that the research has the potential to influence real-world clinical practices, ultimately improving stroke outcomes and contributing to the development of more effective treatment strategies. There are opportunities for patenting and attracting funding for a spinout.

A unique selling point of this project is the opportunity for the successful applicant to work within the Centre for Computational Engineering Sciences, a leading hub for research and education in computational methods. The Centre offers MSc programmes in Computational Fluid Dynamics (CFD), Software Engineering for Technical Computing (CSTE), and Aerospace Computational Engineering (ACE), providing the applicant with access to advanced training and resources in these cutting-edge fields. At the outset of the project, knowledge gaps will be identified, and the applicant will have the chance to attend relevant lectures and develop expertise in CFD and scientific computing. Additionally, depending on the applicant’s interests and aptitude, there is potential to link this research with ongoing virtual reality development, adding an exciting interdisciplinary dimension. Furthermore, the project offers access to conferences, workshops, and collaborative opportunities with leading academic, industrial, and clinical experts, enhancing professional growth and visibility in the field.

The student will gain a broad range of transferable skills that will significantly enhance their employability in both academia and industry. Through the development of advanced simulation tools and machine learning models, the student will gain expertise in computational modelling, scientific computing, and data analysis, as well as experience with software tools like FEniCS and Python. Additionally, working on a high-impact, interdisciplinary project will strengthen problem-solving, critical thinking, and project management skills. The collaboration with experts in academia, industry, and clinical settings will provide valuable networking opportunities and insights into real-world applications, preparing the student for a successful career in healthcare technology, computational engineering, or research. Exposure to virtual reality development and attendance at international conferences will further broaden their skillset, making them highly competitive in the rapidly evolving field of computational healthcare.