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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high
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methods to improve prediction model generalizability, model fairness, and generalizability of fairness across different clinical sites. The researcher will have the opportunity to use machine learning and
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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 4 hours ago
skills and experience and interest in data analysis, data science, machine learning and process automation would be an advantage. Previous experience with XAS or other synchrotron-based techniques would be
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: Expert on steels and steel welding or additive manufacturing Develop advanced machine learning framework to combine different modality and fields of data Conduct CALPHAD-based simulations in a high
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in an area of safe machine learning and/or applications in healthcare Management of a team of PhD students, postdocs, and software developers Coordination of the implementation of research prototypes
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for this position will be a highly motivated individual with experience in deep learning and medical imaging and a PhD degree in computer science, electrical and computer engineering, biomedical engineering
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leverage graph structures to represent, integrate and analyze multi-modal data, employing advanced machine learning techniques to address complex questions in biology. The team is focusing on different
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completion) in AI, Machine Learning, Data Science, Control or Energy Systems Engineering, or a related field. Strong expertise in AI for real-time systems, predictive analytics, or Digital Twins. Experience