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models that include these mechanisms. The postdoc will develop biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM
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biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM data and spatial-omics data collected from state-of-the-art
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policies pertaining to other schools at Duke University. The postdoc candidate is expected to: 1) Develop novel methods for incorporating scientific machine learning in solving problems in solid mechanics
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conduct policies about other schools at Duke University. Compliance with all applicable University and departmental policies and procedures. The postdoc candidate is expected to: 1)Development of machine
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
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27710, United States of America [map ] Subject Areas: Statistics / Statistics Biostatistics / Biostatistics and Data Science Data Science / Machine Learning Appl Deadline: none (posted 2025/02/12
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-performance computing. Additional experience in DNA biophysics, machine learning, and optimization is preferred. This job description intends to provide a representative and level of the types of duties and
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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
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-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