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graphs and network medicine • Translational data science for therapeutic discovery Primary Responsibilities: The Postdoctoral Research Associate is expected to lead and contribute to independent and
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, and EHR data. Experience with modern deep learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, and SciPy. Familiarity with convolutional neural networks (CNNs), graph
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; Build data and knowledge infrastructures, namely knowledge graphs, ontologies and multimodal representations of the CENSE scientific body; Collaborate with the CENSE team and external partners in
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learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images; (b) Designing graph-based inference models capable of reconstructing higher-level logical blocks
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, Create insightful graphs and other graphical tools to present outcomes back to farmers, Develop reporting for specific groups of farmers that are for instance in a specific supply chain or a learning
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) Developing deep learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images; (b) Designing graph-based inference models capable of reconstructing higher-level
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relevant to physics, such as CNNs for image-based field prediction, Graph Neural Networks (GNNs), or Physics-Informed Neural Networks (PINNs) Solid grasp of numerical methods, partial differential equations
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for that month). 2.5. Tasks to be carried out: Development of theoretical approaches in the field of inductive spectral theory and information theory applied to transformer- and graph neural network-based deep
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FAIR/open-science practices, the BrainGlobe ecosystem, EBRAINS, NIH BRAIN Initiative resources, and Brain Maps 4.0 or analogous mesoscale chemoarchitectural atlases. Additional familiarity with graph