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Field
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novel hypotheses, ideas, and develop novel methodologies. Contribute and participate in the authorship of research publications and presentations. Collect, analyze and graph data, conclude research
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), ideally geometric/graph neural networks, equivariant models, or generative models. Interest in applying AI to molecular or biological problems; prior structural biology experience is a plus but not required
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Learning: Apply shape-correspondence and machine-learning approaches, including spectral/graph-based surface alignment and autoencoder-based shape extraction, to compare limb and joint morphology across
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areas: Generative AI Agentic AI Graph Representation Learning and Modeling Foundation Models Large Language Models Multimodal Learning Forecasting Models Basic Qualifications A Ph.D. or equivalent degree
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-learning models (e.g., graph neural networks, equivariant architectures) in collaboration with computer science researchers. Applying developed models to problems in Earth and planetary interiors, such as
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Postdoctoral Researcher in Semantic Knowledge Engineering & Interoperability in Materials Science in
Bundesanstalt für Materialforschung und -prüfung (BAM) | Berlin, Berlin | Germany | about 2 months agotranslated into formal knowledge representations. These form the basis for the development and integration of semantic models into ontologies and knowledge graphs as a semantic layer of federated data
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University. The postdoc will contribute to and help lead projects that may involve concepts and priorities such as knowledge-graph-driven data integration, AI-powered literature review, agentic database search
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: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph
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implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic pathway prediction. Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions
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projects that may involve concepts and priorities such as knowledge-graph-driven data integration, AI-powered literature review, agentic database search assistants, multi-omics analysis tools, life sciences