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validation of single-cell data, and gene regulatory network mapping during neural development. Qualification requirements Applicants must hold a degree equivalent to a Norwegian doctoral degree in Cell Biology
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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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), latch-ups, and the total ionising dose on spiking neural network performance. develop and test fault mitigation strategies, such as spike-based redundancy, reconfigurable neural routing, noise-aware
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geometric understanding of training in deep neural networks. The position offers excellent training opportunities at the intersection of machine learning and applied mathematics. Qualifications: - Applicants
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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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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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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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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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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