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advanced machine learning, with a focus on recommendation systems, graph neural networks, and multimodal AI, by designing novel models and methodologies, and leveraging large-scale structured and
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and computer scientists PhD applicants must possess a Master's degree in mathematics, theoretical physics, or computer science. Candidates should have an exceptional academic record and a robust
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Science, Machine Learning, Computational Linguistics or a related field, if applicable with PhD previous experience in Natural Language Processing, knowledge Graphs, Machine Learning or Recommender Systems
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reconstruct the evolution of particle-laden turbulent flows from limited data using scientific machine learning. You will also be involved in the data-generation and curation for model development. Accordingly
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, finance or computing science/natural language processing/machine learning. Preference will be given to those who are proficient in Python, adept at processing large-scale data and have worked with large
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, imbalanced-data, or distribution-shift settings. Job Requirements: PhD in Computer Science, Data Science, or a closely related field. Strong research background in machine learning and artificial intelligence
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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research, machine learning or artificial intelligence (e.g., large language models, EHR foundation models), causal inference (e.g., target trial emulation), and child health research. The research program
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ideally suited to a PhD-qualified computational biologist or bioinformatician with a strong interest in applying AI and machine learning methods to biological data, who enjoys collaborative, service
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Computational Mechanics. Solid background in continuum mechanics and numerical modeling Strong interest in machine learning and scientific computing Experience with numerical methods for PDEs and data-driven