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genes and molecules to neural networks and psychosocial systems”, PN-IV-P6-6.1-CoEx-2024-0139 Where to apply E-mail [email protected] Requirements Research FieldPsychological sciencesEducation
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genes and molecules to neural networks and psychosocial systems”, PN-IV-P6-6.1-CoEx-2024-0139, Where to apply E-mail [email protected] Requirements Research FieldPsychological sciencesEducation
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genes and molecules to neural networks and psychosocial systems”, PN-IV-P6-6.1-CoEx-2024-0139, Where to apply E-mail [email protected] Requirements Research FieldPsychological sciencesEducation
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opportunities. The chance to help shape a newly funded research line with real clinical impact, within an international network of academic and industry partners. Where to apply Website https
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are not limited to linear discriminants, neural networks, decision trees, support vector machines, unsupervised learning, and reinforcement learning. With examples from real-world applications, students
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Yale University School of Medicine - Department of Neuroscience | New Haven, Connecticut | United States | 6 days ago
intelligence and machine learning are changing all aspects of neuroscience from analysis of neural activity to network modeling to imaging to bioinformatics to molecular modeling. We are enthusiastic about
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
fitted with sensors, we can also access precise physical measurements. Recent work in AI-for-Science has shown that neural-network-based meta-models can also assimilate measurements. Machine learning
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optimize hardware neural networks made of approximately one hundred magnetic tunnel junctions, with radio-frequency inputs, in order to classify RF signals directly in the physical domain. Chains of magnetic
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Duke University - Biochemistry & Cell Biology | Durham, North Carolina | United States | about 2 months ago
positions in deep neural networks for biology. Successful candidates will have a strong interest in developing, interpreting, and applying new AI algorithms and models, motivated by biological research in
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higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming