72 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions at Duke University
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cancer models and patient-centered translational approaches to define mechanisms that regulate treatment response, immune evasion, and disease progression. Candidates with experience in cancer biology
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-edge technologies, including genetically engineered mouse models, patient-derived models, single-cell and spatial genomics, organoid systems, and preclinical therapeutic studies. Learn more about our
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methodologically driven research environment. The position emphasizes the development and application of advanced analytical approaches to address fundamental questions in cancer disparities, while building a strong
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-initiated clinical trials for glioblastoma and other primary brain tumors while developing your own mentored research portfolio. You will work alongside leading clinicians, scientists, surgeons, pathologists
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alongside dedicated faculty and staff who are passionate about developing the next generation of leaders. What Makes Duke Special Collaborative and mission-driven culture Commitment to professional growth and
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architecture, enhancer-promoter communication, disease risk variants, and gene regulatory networks in development, regeneration, and disease. Research models may include patient samples, mouse models, and iPSC
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(e.g. model-aided, convergence analyses, universal), data-driven detection and estimation, robust adaptive filtering / beamforming for radar/sonar under modeling uncertainties. In addition to research
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also develop resource models by compiling relevant datasets and applying data science tools. The Postdoctoral Associate should be able to work effectively with collaborators from diverse disciplinary
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cancer, or related field Preferred Qualifications: Experience working with mammalian cell culture and animal models of cancer (xenografts and GEMMs) Familiarity with next-generation sequencing platforms
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in AI for genomics (e.g., generative models, transformers, genomic language models, agentic AI) and related areas of statistics (e.g., uncertainty quantification for machine learning and AI). Apply