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Field
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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the John Radcliffe Hospital and the Oxford Big Data Institute, with the central aim being the development of rapid diagnostics of antimicrobial resistance in clinical samples. You will work as a member of an
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at the intersection of AI, NLP, and industrial applications. Contribute to the development of scalable and interpretable AI tools for real-world deployment. Qualifications: A PhD in Computer Science, Machine Learning
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learning and its application within the Data Science & AI division (DSAI). With 30+ nationalities and strong industry/academic ties, we offer a dynamic, collaborative ecosystem. The AI and Machine Learning
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the application of machine learning techniques (e.g., doc2vec, encoder models, multi-modal embeddings, large language models) to map concepts and their relationships, tracing how they change, merge, or diverge
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in core methods of machine learning/artificial intelligence. ● Experience with data warehousing and building large, curated datasets with protected health information, suitable for training large
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information systems at large, with a focus on collaborative, data driven, computational and intelligent systems, all with a strong interactive component. The Amsterdam Machine Learning Lab (AMLab) conducts
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 18 days ago
for extension based on mutual interest. We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with
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in core methods of machine learning/artificial intelligence. ● Experience with data warehousing and building large, curated datasets with protected health information, suitable for training large
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-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative