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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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to predict thermal runaway on the cell level. The combustion and gas model developed on the cell level will then feed into the work to accurately predict thermal runaway on pack, module, and system levels
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roughness, AM roughness is characterized by randomness, porosity, and powder adhesion, producing flow behaviors that existing correlations and turbulence models fail to predict. Understanding and modeling
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multimeric complex prediction. You have experience of microbiome sequencing, genome mining, or metagenomic data analysis. You have worked with host-pathogen interaction models, antimicrobial peptides
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very favorable pension. Read more on the university website Project description The goal is to develop AI-based multi-modal prediction models for breast cancer, using both medical images (X-ray