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The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg is a leading international research and innovationcentrein...
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quality modelling, with focus on Knowledge-Guided Machine Learning. The position is a rewarding opportunity to be integrated in an excellent freshwater group. The department’s research and advisory
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We invite applications for a postdoctoral researcher to join the UMLFF project at the University of Luxembourg. The project aims to develop the next generation of uncertainty-aware machine-learning
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, United States of America [map ] Subject Areas: Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Computer Science Machine Learning Appl Deadline: none (posted 2025/08
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 22 hours ago
Position Information General Information Position Number POST40 Working Title Post-Doctoral Fellow-Machine Learning in Thermal-fluid Sciences Division Academic Affairs Department College
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Postdoc in Generative Machine Learning for Biomedical Data | Human Technopole, Milan Build the science that shapes the future of human health. Application closing date: 21.02.2026 Join a place where
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ICT Services & Applications. Your role This position sits at the interface of machine learning, uncertainty quantification and computational biomechanics. You will work within the Legato group and in
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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The Department of Ecoscience at Aarhus University invites applications for two postdoctoral positions to strengthen our research on image recognition, computer vision and deep learning applied
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep