10 software-defined-networking Postdoctoral positions at University of Virginia in United States
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)—topics including, but not limited to: · Physics-informed neural networks (PINN) & neural operators · Physics-aware convolutional neural networks (PARC) · Meta-learning/transfer
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. This position offers a unique opportunity to contribute to high-impact, interdisciplinary research at the intersection of network science, global research competitiveness, and generative AI capabilities
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science of science, network science, and natural language processing. As part of a small research team, the postdoc will help lead efforts to provide a quantitative model of global competitiveness
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university partners on systems-level issues. Run advanced quantitative analyses using statistical software. Generate research questions for analysis utilizing quantitative and qualitative data collected. Lead
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disease (CAD). You will apply expertise in data science, machine learning, as well as multi-omics integration to predict and validate functional regulatory networks in vascular cell types. This work will
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within the PIT CREW and National Center for Rural School Mental Health . Run advanced quantitative analyses using software such as Stata, R, Mplus, HLM, and/or SPSS. Generate research questions
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.), Proteomics, Metabolomics, Bioinformatics. Software: Microsoft Office (power point, excel, word), Image J/Fiji, EndNote. The position will remain open until filled. This is an exempt-level, benefitted
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resonance, computed tomography, and echocardiographic images using a wide range of software programs is essential. Additionally, a strong understanding of cardiac anatomy to facilitate image segmentation and
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within broader gene networks regulating islet autoimmunity. This project involves generating data from longitudinally collected human peripheral blood mononuclear cells at the single-cell level (CITE-seq
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data issues. Utilizing machine learning techniques as appropriate for data analysis. Developing computing programs and software to support research initiatives. Applying new methodologies to real-world