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reproducible infrastructure while collaborating closely with cross-disciplinary teams across genomics, epidemiology, machine learning, and biomedical science. The role will also involve mentoring junior
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digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different conditions. To propose a methodology/framework in a
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Machine Intelligence (CVI²) research group (CVI² Group ), led by Prof. Djamila Aouada, to pursue a PhD in Computer Vision with a focus on Media Forensics and Deepfake Detection. The candidate will conduct
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, software engineering, Embedded Systems, Artificial Intelligence / Machine Learning, data science, or closely related disciplines or significant practical/industrial experience. Significant postdoctoral
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Imaging, Machine Learning, or a related field • Demonstrated research experience in generative models for medical imaging (e.g., diffusion models, VAEs, GANs) • Publications in high-ranking journals and
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and Liu, Supervised learning in physical networks: From machine learning to learning machines, PRX 11, 021045 (2021) [2] Stern and Murugan, Learning without neurons in physical systems, Ann Rev Cond
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Description VIB.AI, the VIB Center for AI & Computational Biology, is a young research center dedicated to combining machine learning with in-depth knowledge of biological processes. Our mission is
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research. Experience applying machine learning and statistical modeling techniques to large biological datasets for biomarker discovery, disease prediction, or host-pathogen investigations. Proficiency in
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 4 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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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