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Field
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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to date focus on just one layer, understanding what keeps AF going is challenging. This PhD project aims to bridge that gap by combining advanced machine learning tools with a new experimental protocol
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through publications and presentations at leading conferences. This project will be undertaken in collaboration with Dr Feras Dayoub of the Australian Institute for Machine Learning, and Advanced Systems
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, government entities, industry partners, NGOs and citizens – to collaboratively make sustainable change. Through transdisciplinary action research, the consortium investigates conditions for collective learning
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HPC environments Good communication skills to interact with collaborators ranging from machine learning researchers to pathologists or medical students Knowledge of biology and medicine is a plus Highly
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analysis (R, Python) is an asset. Curiosity, creativity, rigor, willingness to learn, team spirit and collaborative capacity, excellent time and priority management. Fluency in English (written and spoken
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for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
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. With over 26,000 students and more than 4000 scientists, research, teaching, and learning dedicated to the advancement of science and technology have been conducted here for more than 200 years, guided
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Containers (e.g. docker). * Proven ability to work independently and collaboratively. * Experience with communicating results and concepts with a diverse audience including molecular biologists and laboratory
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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. TUD has established the Collaborative