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mliuttu銅蟲 (小有名氣)
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招一名博士生(RA) 2025春季入學(xué)University of Miami 計(jì)算機(jī)系
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學(xué)校：University of Miami 排名: #67 in National Universities by usnews 院系：Computer Science 導(dǎo)師: Liang Liang (https://liangbright.wordpress.com/) 實(shí)驗(yàn)室硬件條件： Specifications of server 1: 2 x Intel E5-2698 v4 CPUs, 4 x Nvidia Tesla V100 (32GB VRAM) GPUs, 256GB DDR4 ECC Memory, and 4 x 2TB SSDs. Specifications of server 2: 2 x AMD EPYC 7313 CPUs, 6 x Nvidia RTX A6000 (48GB VARM) GPUs, 512GB DDR4 ECC Memory, 6 x 7.68TB SSDs, 2 x 18 TB HDDs. Specifications of server 3: 2 x AMD EPYC 9554 CPUs, 4 x Nvidia H100 (80GB VRAM) GPUs, 1.5TB DDR5 ECC Memory, 4 x 7.68TB SSDs (more GPUs will be added) 這名博士生在指導(dǎo)下從事如下研究： A digital twin is a virtual model that mirrors and updates in real-time based on data from its physical counterpart. In biomedical and healthcare fields, digital twins, representing virtual models of patients, medical devices, and more, can open up new avenues for developing and evaluating innovative biomedical technologies, particularly enabling virtual clinical trials for evaluating cardiovascular medical devices and advancing regulatory sciences. However, current digital twin technologies lack sufficient computational fidelity and efficiency to effectively support these biomedical and healthcare applications. To resolve these challenges, this project aims to develop advanced computational methods for creating high-fidelity, fast-running digital twins of patient hearts and cardiovascular medical devices. Additionally, the methods will be made publicly available through a software/cyberinfrastructure platform. This will facilitate virtual clinical trials that can evaluate the efficacy and safety of medical devices, as well as improve device designs before initiating real clinical trials in a safe, cost-effective, and precisely controlled manner. In addition to advancing digital twin technologies, the project's cyberinfrastructure will serve as an educational resource for students, researchers, and industrial engineers to enhance their understanding of advanced digital twin techniques for medical device evaluation. This project will develop novel machine learning (ML)-based image analysis algorithms and physics solvers for performing near-realtime virtual clinical trials with high-fidelity digital twins of patient hearts and cardiovascular medical devices. Patient-specific geometries and tissue mechanical properties will be incorporated into the digital twin construction for near-realtime physics simulations. Consequently, virtual clinical trials can be performed at significantly reduced time and financial costs. This project will deliver (1) novel ML algorithms for accurate digital twin geometry reconstruction from 3D+t medical images, enabling point-to-point mesh correspondence for high-fidelity dynamic motion tracking; (2) a robust and computationally efficient inverse method to identify in vivo material properties from medical images, which is essential for creating material-realistic digital twins; (3) a new ML-based fluid-structure interaction (ML-FSI) solver for biomechanics and hemodynamic analyses, thereby enabling dynamic digital twin simulations throughout a cardiac cycle. While the primary focus will be on digital twins of the left heart and aorta, the computational methods can be generally applied to create digital twins of the entire heart. The computational methods will be demonstrated through concrete examples involving Transcatheter Aortic Valve Replacement (TAVR) and Thoracic Endovascular Aortic Repair (TEVAR) devices. The algorithms and methods developed in this project will be generic and readily applicable to devices for treating various cardiovascular diseases. This project is jointly funded by the Division of Mathematical Sciences, the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program, and the CBET Engineering of Biomedical Systems program. 計(jì)算機(jī)系PhD錄取基本要求(請(qǐng)仔細(xì)閱讀） https://csc.as.miami.edu/programs/doctor-of-philosophy/index.html 從事這項(xiàng)科研任務(wù)，需要如下基本條件 (1) 核心數(shù)學(xué)課成績(jī)>80 包括 微積分 線性代數(shù) 概率論 (2) 編程能力強(qiáng) (主要用Python) 本科、碩士 都可以申請(qǐng) 聯(lián)系方式: liang@cs.miami.edu 請(qǐng)發(fā)送成績(jī)單和CV的電子版 |
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