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Assessment of Cardiac Function and Outcome Prediction using Artificial Intelligence and Echocardiography". The project aims to develop novel AI models based on self-supervised learning and multimodal machine
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mechanistic process models with machine learning for accuracy, generalization, and interpretability. Uncertainty-aware AI: robust inference under noise, drift, and changing conditions; knowing when a model is
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that includes material space construction and exploration, candidate selection and verification, providing data for machine learning models to optimise membrane properties, structure, and fabrication. The fellow
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, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling. The centre has numerous collaborations with leading biomedical research groups internationally and in
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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling
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outcomes and economic performance, specifically addressing challenges such as overdiagnosis in cancer care. We will utilize economic theory, simulation, economic evaluation and machine learning to quantify
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. Strong (inter-)national network in field of application. Experience with high-performance computing (HPC) and large datasets. Experience with machine learning applied to geophysical signals. Experience in
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while offering flexibility to tailor your training to specific needs and interests through elective courses and secondments. • Blended Learning Approach: Our training combines intensive in-person
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promise and peril of hybrid intelligence—humans and machines working and learning together. Our mission is to establish an internationally leading interdisciplinary hub that advances foundational research
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machine-learning methods to enhance predictive capability and enable adaptive process control. Experimental work will include laboratory- and industrial-scale forming trials, supported by comprehensive