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Field
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Engineering, or related field. At least 3 years of relevant experience in computer vision, artificial intelligence, etc. Proficiency in programming languages such as C and Python Proficiency in deep learning
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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MACQUARIE UNIVERSITY - SYDNEY AUSTRALIA | North Ryde, New South Wales | Australia | about 16 hours ago
differential geometry, algebraic geometry, etc.) or for computer science (such as machine learning, linear logic, etc.). While the position start date is flexible, the successful applicant must have completed
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About the Opportunity About the Opportunity Northeastern University Pharmaceutical Industry Fellowships Program is a two-year experiential program designed to advance lifelong learning and the
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conferences and journals, specifically in Computer Vision and/or Natural Language Processing Proficiency in python and deep learning libraries (pytorch) Proficiency in git, Docker, python, and SQL Experience in
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About the Opportunity About the Opportunity Northeastern University Pharmaceutical Industry Fellowships Program is a two-year experiential program designed to advance lifelong learning and the
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Intelligence & Machine Learning Develop machine learning and AI models to identify, predict and characterise genome instability patterns. Design generative and predictive computational models to infer mutational
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materials discovery with cutting-edge high-throughput platforms, robotics, machine learning, and autonomous experimental workflows. Access World-Class Facilities: Based in the Department of Chemical and
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) years in Computer Science, Data Science, Computational Engineering, Bioinformatics, Computational Social Science, or another highly quantitative field with substantial emphasis on machine learning, deep
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The Research Fellow (RF) will conduct research in the field of coastal dynamics, with a focus on the development and application of machine-learning-enhanced coastal models. The project aims