30 machine-learning-modeling-"Linnaeus-University" Postdoctoral positions at Virginia Tech
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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches
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models and machine learning techniques for kinetic equations arising from plasma and neutron transport. The position will be based at Virginia Tech’s campus in Blacksburg, VA. The postdoc will have a
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. - Strong proficiency in machine learning, optimization algorithms, and computational modeling applied to construction systems. - Experience with designing and conducting experimental studies to evaluate
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jurisdictions utilizing land use-value assessment estimates. Duties include, but are not limited to: development of computational methods, maintenance of current models and data sets, identifying and testing
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candidate will play a key role in developing and advancing new models and simulations for Computational Fluid Dynamics (CFD) hypersonic codes. Specific tasks include developing new turbulence and transition
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molecular dynamics simulations. This position emphasizes research in the modeling of complex chemical systems, where the candidate will integrate advanced simulation techniques with modern machine learning
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CRISPRai and optogenetic control systems and developing predictive metabolic models for the oleaginous yeast Yarrowia lipolytica. This position offers a unique opportunity to conduct cutting-edge research
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datasets, machine learning, and experimental methods to investigate how the tumor microenvironment and gene regulatory factors control tumor metastasis cascade. By advancing our understanding of malignant
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work with colleagues from Virginia Tech and other collaborating universities to run, analyze, and model human evacuation experiments. Data will include motion capture and psychophysiology data to explore
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and optimization of measurement-based quantum computing protocols for quantum simulation of quantum many-body models. Preference will be given to candidates familiar with the stabilizer formalism and