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analysis methods, including quantitative structural analysis, network-based functional analysis, voxelwise cross-modality analyses, graph theoretic approaches, and machine-learning based applications
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multiple NIH grants, including the NIH Human Immunology Project Consortium (HIPC), a highly collaborative national network that the successful candidate will join. Available areas of research include both
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complex algorithms and predictive models and determine analytical approaches and modeling techniques to evaluate potential future outcomes. Establish analytical rigor and statistical methods to analyze
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the direction and supervision of Prof. Lucila Ohno-Machado, we seek to appoint full-time Postdoctoral Associates (two positions) in the areas of privacy-preserving health data sharing, artificial neural network
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analyse complex data from emerging genomic pathology approaches, including CRISPR, single cell sequencing, spatial transcriptomics, and image analysis to address biologically- and clinically-driven
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of mutational processes in human health and disease. We are an interdisciplinary team of experimental, computational, and clinician scientists allowing us to generate and analyse complex data from emerging
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residues in their catalytic sites responsible for dephosphorylation. We have identified allosteric inhibitors for MKP5 and determined their structure in complex with these inhibitors. The goal is to
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Postdoctoral Associate in Computational Neuroscience at Yale The Post-Doctoral Associate will develop predictive, gene-regulatory networks linked to Parkinson’s and other neurological diseases in
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-on experience in blockchain technology (preferably Ethereum) and/or machine learning. Solid foundation in computer network and/or data analysis. Strong programming skills, preferably in Java. Ability to work
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development reviews, leadership and entrepreneurship training, collaborative training opportunities, and networking and conferences. Qualifications: PhD in Molecular Biology, RNA Biology, Biophysics, or a