Assessing risk of heart failure with cardiac modelling and statistical inference

近年来，我们在复杂生理系统的数学建模方面取得了令人印象深刻的进展。然而，参数估计和不确定度量化仍然具有挑战性。这个博士项目将让你有机会加入一个跨学科的研究团队，为心脏力学模型的计算建模和推理开发新的方法。您的最终目标将是为基于复杂数学生理模型的心脏病风险评估的新一代临床决策支持系统铺平道路。您的目标是使用磁共振成像数据实现这些模型的实时患者特定校准。可靠的不确定性量化对知情的风险评估将是至关重要的。对于具有较强的应用数学和统计学或计算机科学背景的研究生来说，这是一个理想的博士项目，他们对计算力学感兴趣，并将机器学习和贝叶斯建模的前沿推理和模式识别方法应用于具有挑战性的心脏力学建模问题。该项目将为您提供加入跨学科研究中心的机会，该中心旨在通过将尖端的数学和统计建模带入临床，推动定量医学的界限并改善心血管医疗保健。

In recent years, we have witnessed impressive developments in the mathematical modelling of complex physiological systems. However, parameter estimation and uncertainty quantification still remain challenging. This PhD project will give you the opportunity to join an interdisciplinary research team to develop new methodologies for computational modelling and inference in cardio-mechanic models. Your ultimate objective will be to contribute to paving the path to a new generation of clinical decision support systems for cardiac disease risk assessment based on complex mathematical-physiological models. You will aim to achieve patient-specific calibration of these models in real time, using magnetic resonance imaging data. Sound uncertainty quantification for informed risk assessment will be paramount. This is an ideal PhD project for a postgraduate student with a strong applied mathematics and statistics or Computer Science background who is interested in computational mechanics and adapting cutting-edge inference and pattern recognition methods from Machine Learning and Bayesian modelling to challenging cardio-mechanic modelling problems. The project will give you the opportunity to join a cross-disciplinary Research Hub that aims to push the boundaries of quantitative medicine and improve cardio-vascular healthcare by bringing cutting-edge mathematical and statistical modelling into the clinic.