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other duties related to the research program. Job Requirements: A PhD in Computer Science or a relevant field. Strong background in deep learning, Generative AI, and multimodal learning. Strong
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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Intelligence, Machine Learning, or relevant fields. Strong theoretical research capability, particularly in the theoretical analysis of optimization, convergence, stability, and/or generalization of deep
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dysbiosis drives immune dysregulation and disease progression in pediatric patients, generating new clinical multi-omics data and using deep learning, structural equation models, and causal inference
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related to heart failure and cardiovascular biology. Develop and apply machine-learning and deep-learning approaches to identify disease-associated cardiomyocyte subtypes, cellular trajectories, and
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concepts into accessible, domain-relevant learning experiences for students. The full job description is available here: https://computing.mit.edu/lecturer/ Job Requirements REQUIRED: PhD in Computer Science
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 14 hours ago
Do you want to help transform human health through machine learning and life science approaches? Join AITHYRA in Vienna for a fully funded PhD at the intersection of machine learning, experimental
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Computer Science, Artificial Intelligence, Mathematics, Engineering, or a related field. Entry level candidates with demonstrated expertise in artificial intelligence (AI), machine learning, deep learning
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, computational efficiency, and deployment of large AI models while maintaining high model performance. The ideal candidate has a strong research background in large language models (LLMs), efficient deep learning
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Distributed Systems Artificial Intelligence and Intelligent Systems Machine Learning and Deep Learning Natural Language Processing Responsible AI, Ethics and Human-Centred Computing Capstone AI Innovation