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to work effectively in an interdisciplinary team. PREFERRED QUALIFICATIONS Experience with one or more of the following: knowledge graphs, graph machine learning, link prediction, representation learning
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is to develop computationally efficient reduced-order dynamical systems on graph with modern power grid systems as an application. Education and Experience: Applicants must have recently completed a
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Zerbib. This includes graph theory, discrete geometry, topological combinatorics, extremal combinatorics, and flag algebras. The position has a 2-1 teaching load and a requirement to be involved with
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systems architecting AI/ML-driven clinical and operational decision support Digital health and learning health systems Healthcare operations, resource allocation, and workflow optimization Network, graph
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
immunotherapies, integrating graph neural networks, regulon-aware pooling, and transfer learning with biological regulatory networks. 4) Developing and validating computational biomarkers (IGR burden, TAA burden
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systems • Healthcare operations, resource allocation, and workflow optimization • Network, graph, and agent-based modeling for care delivery • Health equity, patient access, and system resilience • Multi
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Term: Initially 1 year, renewable. Appointment Start Date: As early as February 2026, but flexible Group or Departmental Website: https://med.stanford.edu/bridge-lab.html (link is external) How to Submit
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critical thinking and problem-solving skills. Outstanding presentation skills. Experience in graphing, statistical analysis and data management skills. Proficient in Word, Excel, GraphPad, Adobe Photoshop
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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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scientific writing skills. Design and performance of experiments, creating graphs, knowledge of statistics, interpretation and dissemination of data. 3-years of mentorship of junior technicians and trainees