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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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Machine Learning for both supervised and unsupervised algorithms. Deep understanding of principles and best practice in machine learning, with a focus on NLP especially in sequence labelling tasks based
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modalities and populations. This project (SpyGlass) will investigate machine learning methods, particularly generative and foundation models, to discover robust correlation structures in cross-modal datasets
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 271598471 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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: Machine learning/deep learning model development for biomolecular data analyses and prediction Research Area: Data science and computational chemistry Required Skills: A Ph.D. in relevant field within