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Field
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measurement technique development, atmospheric modelling, and advanced methods for integrating observational and model data through data assimilation and machine learning. About the research project The overall
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–based, web-enabled decision-support tool for farmers, using machine learning (random forest) predictive models. Working closely with the PhD student and PIs to ensure rigorous model development
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data
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Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence modeling architectures and interpretable AI methods (SHAP, Integrated
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: Durham, North Carolina 27708, United States of America [map ] Subject Areas: Computer Science / Augmented Reality , Programming Languages Electrical and Computer Engineering / Machine Learning Appl
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measurement technique development, atmospheric modelling, and advanced methods for integrating observational and model data through data assimilation and machine learning. About the research project The overall
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in data integration, model design, and large-scale training by combining multi-modal scientific data, knowledge graphs, physics-aware machine learning, and GPU/HPC computing to develop transparent and
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Zanna, the successful candidate will focus on developing generative machine learning models for complex dynamical systems for probabilistic forecasts. The postdoc will be expected to lead independent
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Responsibilities 1. Research and Innovation:Conduct cutting-edge research in AI, machine learning, and multimodal learning, with a focus on developing Large Language Models (LLMs) that process and generate
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with experimentalists to validate predictions made by their machine-learning models and drive wet-lab discoveries. The candidate may also have opportunities to work with research software engineers