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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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/Anders Lien 16th October 2026 Languages English English English PhD Research Fellow in Machine Learning and Statistics Apply for this job See advertisement About the position Integreat - the Norwegian
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, Mathematics, Physics, or a related discipline Excellent programming skills in Python and/or R Strong expertise in machine learning, deep learning and predictive modelling Experience working with multimodal
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University of Science and Technology (NTNU) has a vacant position as PhD candidate in the field of machine learning for materials science. Your immediate leader will be the Head of Department. About the
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(especially related to applications of artificial intelligence), and real-world experiences so students not only accumulate knowledge but also develop the commitment and desire to apply what they learn in
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-resolution or single-molecule imaging. ● Experience with machine learning/deep learning for bioimage analysis, including model application, validation and/or development. ● Experience with optical development
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above) grades. You have a strong background in deep learning. Previous experience with robotics, world models, reinforcement learning or other ML-based techniques for robot control is considered a plus
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 hours ago
campus, world-class medical care, commitment to the arts and top athletic programs, Carolina is an ideal place to teach, work and learn. One of the best college towns and best places to live in
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access to the University's research resources, working under the mentorship and guidance of Associate Professor Courtney Fung, PhD, and Honorary Professor Bates Gill, PhD. The fellowship is structured
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
but are not limited to: development of new AI architectures for biology and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and