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Field
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& Machine Learning • Clinical pathways and decision support for patients with acute chest pain • AutoPiX – Explainable Deep Learning for Multimodal and Longitudinal Imaging Biomarkers in Arthritis • Speaking
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in MRI sequence programming, preferably using the Siemens IDEA platform, is a plus, particularly for projects involving sequence development. Experience applying deep learning techniques
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-year position dedicated to the project “Foundations for Adaptive and Generalizable Deep Learning” (EXPLORA), funded by Ministerio de Ciencia, Innovación y Universidades/AEI, focused on ‘Continual
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knowledge with statistics, machine learning, Deep Learning and AI are an advantage • Good knowledge of the English language LanguagesENGLISH Research FieldEnvironmental science » Ecology Additional
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or Python) • Good knowledge of the English language • Experience with statistics, machine learning, Deep Learning and AI are an advantage • Familiarity with fundamental ecological concepts and experience in
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(e.g., Computer Science, Software Engineering, Machine Learning, Artificial Intelligence, Computational Linguistics, or a related field) Strong skills in machine learning and deep learning Experience
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, combining both accuracy and explainability; (3) extend statistical learning theory to offer theoretical bounds for intrinsically-aligned AI models; (4) employ the newly-developed metrics to train deep neural
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teaching assistantship. The ideal candidate will have a strong foundation in Python programming and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch. Applicants with a
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, willingness to learn, and the ability to think creatively about complex physical systems are just as important as specific technical expertise. This PhD project—High-Fidelity Simulations of Geological CO2
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; Machine Learning• Clinical pathways and decision support for patients with acute chest pain• AutoPiX – Explainable Deep Learning for Multimodal and Longitudinal Imaging Biomarkers in Arthritis• Speaking