28 modeling-and-simulation-"UNIVERSITY-OF-SOUTHAMPTON" Postdoctoral positions at Duke University
Sort by
Refine Your Search
-
implement computational models and perform deep learning analyses for the early detection of Alzheimer’s disease using MRI, biomarkers, and advanced computational techniques · Collaborate with a
-
, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
-
Description Post-Doctoral Research Associate in modeling materials effect in Urban Heat Island Job Description: A post-doctoral research position is available at Duke University for a candidate with expertise
-
differentiation and melanoma and multiple myeloma biology utilizing cultured cells and animal models of skin diseases. Work Performed • Development of new and implementation and modification of existing
-
cultured human and rodent cardiomyocytes, engineered heart tissues, and animal models of heart development and disease. Specifically, you will engage in basic science and applied research to explore
-
pathways and mechanisms underlying autoimmunity from a lncRNA and epigenetic gene regulation perspective. We utilize biochemical assays, tissue culture, mouse transplantation & disease modeling experiments
-
basic research and animal models, preferably rodents. The ideal candidate will have a strong background and experience in the field of neuroscience and behavior in rodent models, such as social behavior
-
, and to interact regularly with Dr. Jonathan Campbell to design and execute experimental studies involving animal and cell-based models of metabolic disease. In addition, will also perform the following
-
-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine learning is desired
-
computational and data analytical methodology development and implementation; experience in supervised and unsupervised machine learning, low-dimensional models or deep learning models, and willingness to learn