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cutting edge scientific and technical progress and participate in our international networks. Experience our atmosphere of tolerance and respect. Salary and benefits are similar to those of public service
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classes and their roles in scientific applications, such as deep neural networks (DNNs), convolutional neural networks (CNNs), transformer models, and graph-based neural networks. Familiarity with software
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Max Planck Institute for Demographic Research, Rostock | Rostock, Mecklenburg Vorpommern | Germany | about 1 month ago
. Support for relocation costs is available. The MPIDR is one of the leading demographic research centers in the world. It is part of the Max Planck Society , a network of 85 institutes that form Germany’s
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study of daily well-being processes in a lifespan sample of St. Louis community adults and survey data collected from the longitudinal St. Louis Personality and Aging Network (SPAN) study. In addition
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applications between the cloud and the network edge. In 8RA, FBK is responsible for the project Cybersecurity & AI at the Edge (CAITE) whose aim is twofold. First, it aims to bring AI at the edge, by providing
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electrochemistry and/or electrolysis Strong hands-on experience in material characterization and/or chemical analysis Willingness to pro-actively collaborate in a network of academia and industry Strong written and
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studies, and user research Perform sequence analysis, network analysis, and pattern mining to identify team-level interaction motifs Write and prepare papers for submission to peer-reviewed journals and
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and their trade-offs in autocatalytic RNA reaction networks." Nat. Comm. 12, 842 (2021). Arsène, Simon, et al. "Coupled catabolism and anabolism in autocatalytic RNA sets." Nucleic Acids Res. 46, 9660
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | about 3 hours ago
qualified Post-Docs interested in Computational Social Science. This application call is targeted to the broad topic of artificial intelligence, computing, and society. Research Themes and Teams The Network
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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