72 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions at Duke University
Sort by
Refine Your Search
-
development, validation, and implementation of artificial intelligence models using multimodal imaging and clinical datasets. What You'll Do: Data Acquisition & Management Organize, curate, and maintain large
-
collaborative modeling of marine mammal species distributions, with a focus on developing management-ready results and accompanying manuscripts suitable for immediate application to ongoing protected species
-
new NIH-funded Center for Excellence in Multiscale Immune Systems Modeling . This position focuses on the development, calibration, and analysis of multiscale agent-based models (ABMs) and differential
-
development of measurement, evaluation, and analytic strategies to inform policy and systems transformation – 45% Develop conceptual frameworks, logic models, measurement plans, and analytic strategies, drawing
-
mechanisms that contribute to chronic pain, inflammation, and neuroinflammation. In this role, you will play a key part in advancing discovery-driven research that integrates molecular genetics, behavioral
-
creativity, rigorous scholarship, and collaboration. You'll have opportunities to develop new skills, contribute to high-impact publications, and expand your professional growth while making meaningful
-
, 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-derived
-
underlying juvenile dermatomyositis and related pediatric conditions. Through the integration of advanced molecular techniques, patient-derived samples, and functional muscle modeling, you will help develop
-
perform daily activities which include, but may not be limited to: Develop AI agentic systems that can automatically extract and generate metadata for computational models from sources such as: Code
-
on identifying and validating surrogate endpoints for overall survival using data from cancer clinical trials and patient registries, developing prognostic models of clinical outcomes in cancer, and conducting