Susann Beier

Dr. Susann Beier is a renowned researcher in the field of biomedical engineering, specialising in cardiovascular health and machine learning. Her outstanding contributions to the area are evidenced by her prestigious awards, including the International Cardiovascular Innovations Award and the National Heart Foundation Research Fellowship in New Zealand. As a Fellow of the Cardiovascular Society of Australia and New Zealand, Dr. Beier has been recognised as a leading expert in cardiovascular health and has made significant strides in advancing the field. Dr. Beier's research focuses on risk assessment and prevention in cardiovascular health through medical image analysis and advanced computational biofluids modelling using high-performance computing. Her work is fundamental and applied, strongly emphasising developing innovative solutions that address cardiovascular health challenges. Through her leadership as the head of the Sydney Vascular Modelling Group (SVMG), Dr Beier has spearheaded groundbreaking research projects that leverage the latest advances in machine learning and AI to improve cardiovascular health outcomes. The group's cutting-edge research can revolutionise the biomedical and bioengineering field. With over 75 peer-reviewed research outputs, Dr. Beier's work has been widely recognised and has significantly impacted the field. As the Associate Editor of Biomedical Engineering Online, she continues contributing to the scientific community by publishing new research and mentoring the next generation of researchers. As a result, Dr Beier's research presents exciting opportunities for PhD projects that will allow students to contribute to the cutting-edge work of the Sydney Vascular Modelling Group and advance the field of biomedical and bioengineering. By combining expertise in machine learning, AI, and biomedical engineering, students can make a meaningful impact on preventing and treating heart attacks and other cardiovascular conditions. PHD PROJECTS AVAILABLE. biomedical engineering, cardiac & cardiovascular systems, effective use of digital technology, biomedical implants, machine learning, fluid mechanics, immersive environment; Cardiology (incl. cardiovascular diseases), Biomedical engineering, Bio-fluids, Machine learning