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. Job Description: A postdoctoral position is available in the laboratory of Catherine Marcinkiewcz, Ph.D. at the University of Florida. Research in the Marcinkiewcz lab focuses on unraveling neural
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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Differential Equations, and Graph Neural Networks. The objective is to measure and predict evolutionary forces and spatial cell interactions in healthy versus cancerous tissues, ultimately identifying
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields, including robotic control, fluid mechanics and
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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CBS - Postdoctoral Position: Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient
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models, programming, and quantitative methods. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We