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, Atmospheric Science, LSTMs, VAEs, PyTorch, TensorFlow, NumPy, pandas, scikit-learn, spatio-temporal datasets Additional Information Eligibility criteria Mandatory: *A PhD in Data Science, Artificial
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 18 hours ago
cleaning, denoising, and prediction, including approaches based on statistical machine learning, deep learning, and Transformer architectures. Perform signal analysis in both the Fourier (frequency) domain
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traditional custodians of the land, sea and waters of the areas upon which we live and work. We recognise their valuable contributions and deep connection to country and pay respect to Elders past and present
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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