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Field
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disciplines with PhD Extensive knowledge of machine learning/artificial intelligence and big data science Extensive knowledge of programming languages (ideally Python) Basic knowledge of synchrotron research
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multiple research groups with research interest in fundamental biomedical research (Centre d’innovation biomédicale; CIB), bridging neurosciences and artificial intelligence (Union Neurosciences et
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: Artificial Intelligence / Machine Learning Knowledge Representation and NLP methods Clinical Informatics Bioinformatics Biomedical Ontology Public Health Informatics Nursing Informatics Imaging Informatics
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detection equipment to support sponsors in operation and research. In addition, the Group also works on artificial intelligence and machine learning to address questions related to detection and safeguards
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
the ability to design and implement novel artificial intelligence algorithms. Examples of research directions could include: multimodal foundational models for biomedical data, deep-learning architectures
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
Experience Requirements Applicants should have (or expect to receive before the start of the position) a PhD in Computer Science or EE/ECE on a topic in machine learning, artificial intelligence, natural
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develop and apply computational approaches for mass spectrometry data, with artificial intelligence/machine learning (AI/ML) being a major focus. They will have an opportunity to lead and contribute to a
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will use our Campus Values to guide their decisions and actions and demonstrate our Rebel spirit. PREFERRED QUALIFICATIONS Experience applying machine learning (ML) or artificial intelligence (AI
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holding a PhD in chemical, environmental or process engineering, to apply for a full-time post-doctoral fellowship position in the field of water desalination, focusing on the development of an artificial
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in the Department of Urology at the Stanford University School of Medicine and will be affiliated with Department of Radiation Oncology and the Center for Artificial Intelligence in Medicine & Imaging