Research Associate Data Scientist

This is a unique opportunity for an early career biomedical engineer wishing to develop their research with a multidisciplinary focus, working at the intersection of Artificial Intelligence, data science and biomedical modeling. This post is funded by a UKRI Project Grant. You will be part of CVD Net and will work collaboratively with other institutions including the Alan Turing Institute, University of Sheffield and University of Nottingham to design and build cardiac digital twins from health data to accurately track changes to disease progression and responses to treatment for patients with pulmonary arterial hypertension (PAH). You will also be part of the Cardiac Electro-Mechanics Research Group (CEMRG) at Imperial College London which applies statistical, machine learning and simulation approaches to combine experimental and clinical data with physics and biology to study the physiology, pathology, diagnosis and treatment of the heart. In this post, the you will work at the interface of artificial intelligence, data science, and biomedical modeling by support the development of data management pipelines and the application of large language models (LLMs) for automating literature-based data extraction, facilitating the calibration of computational models for digital twins of the heart. Your role involves building AI-driven pipelines to extract, clean, and structure data from scientific literature and clinical datasets for model calibration. This work supports the development of digital twins to enhance cardiovascular health research and improve personalized treatments. This role will support accelerating the creation and validation of cardiac digital twins by improving the efficiency and accuracy of data acquisition, integration, and utilization. You wil collaborate with clinicians, engineers, and data managers to ensure that the data aligns with the the needs of research and clinical applications.

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