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Be You. At Duke, we celebrate individuality and the unique perspectives that each member of our community brings. As the Machine Learning Research Data Analyst, Ophthalmic Imaging, you'll play a
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field, with expertise in one or more areas of trustworthy AI, such as human-AI interaction, fairness, transparency and interpretability, privacy, or robustness. A strong understanding of machine learning
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artificial intelligence for science, knowledge-guided machine learning, and scalable scientific AI workflows. The successful candidate will conduct research at the intersection of machine learning, high
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Inria, the French national research institute for the digital sciences | Grenoble, Rhone Alpes | France | about 17 hours ago
communication, sociable with an appetite for working in a group. Additional skills appreciated: rigorous, organized, curious, autonomous, proactive and dynamic. A specialization in optimization, machine learning
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join a highly collaborative, interdisciplinary research environment focused on developing and applying cutting-edge machine learning and deep learning methods to abdominal imaging. Working alongside
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for flexibility in the start and end dates, subject to approval by the department and the funding agencies. The successful candidate will join the Machine Learning & Data Science research group and conduct research
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You'll Bring Required Qualifications PhD or equivalent doctoral degree (ScD, MD, DVM, or related terminal degree). Strong background in one or more of the following: Computer Vision Machine Learning
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observational data of non-CO2 greenhouse and ozone-depleting gases (e.g., N2 O, CFCs, HFCs), and to extract diagnostic insights from instrument performance indicators, using machine learning methods. Additional
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. The ideal candidate will possess not only a deep conceptual understanding of neuroscience but also advanced technical expertise in machine learning, artificial intelligence, and data modeling approaches. We
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. We envisage combining process-based modelling and machine learning, as well as integration of Earth observation data into the modelling framework. The objective is to quantify the GHG budgets