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Field
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Associate selected for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins
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computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate hydraulic flood
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., model-based, optimal, learning-augmented control), perception, and planning modules; run structured experiments and benchmarking. Architect high-quality research codebases in C++/Python/C# (e.g., ROS/ROS
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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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positions: Qualifications 1. Computational Biology & Data Integration Using multi-omics microbiome analysis, single-cell transcriptomics, and machine learning–based biomarker prediction models, this project
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. Applicants with experience in Bayesian modeling, spatial statistics, mathematical modeling, data integration, uncertainty quantification and/or machine learning are encouraged to apply. The postdoc will also
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, execution, and data interpretation) Numerical modeling and simulation to complement physical experiments Preferred) Proficiency in CAD and modeling tools such as SolidWorks for experimental design and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
prediction using large-scale multimodal neuroimaging data. The research will emphasize methodological innovation in statistical modeling, transfer learning, machine learning, model ensemble strategies, and
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and as part of a collaborative, interdisciplinary team. Commitment to publishing research and pursuing a career in academic or translational research. Experience with statistical modeling, machine
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD