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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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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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. This role enables postdocs to gain expertise in causal analysis within complex, non-probability observational samples while engaging in exciting applications that harness and integrate data from various
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professional network, preparing for leadership roles in academia, policy, or industry. Receive the IT support required by their research (e.g. specialized software or computation time). Additional Information
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in candidates with interest in algorithms, blockchains and cryptocurrency, causal inference, game theory, learning, machine learning, market design, and networks, but all subjects at the intersection
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, artificial neural network models, evaluation of individualized predictions or estimates for individuals with diverse backgrounds, and promotion of health equity using AI models. The Postdoctoral Associate will
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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