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in Economics, Mathematical Sciences, Computer Science, Environmental Science (with a quantitative focus) or related fields strong experience in large-scale data processing and management, including
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 17 hours ago
mathematical ADA model to provide quantitative immunogenicity assessment for therapeutic proteins. This model was constructed using antibody titer data from subjects across 10 clinical trials, with ADA time
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-disciplinary areas of artificial intelligence machine learning big data and data analytics software and security mobility and autonomy The Presidential Postdoctoral Fellowship is proudly supported by generous
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analysis, data simulation and imputation. B) Participate in development and testing statistical methods for admixed human datasets. C) Omics (genome, transcriptome, microbiome, epigenome) data integration
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team members to ensure all project deliverables are met. Accumulate data from Co-PIs for quarterly, annual, or final progress reports Schedule meetings with co-PIs, collaborators on-site or off-site
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team members to examine patients’ responses by analysing EEG and behavioural data. Candidates are expected to hold a medical qualification (MBBS or equivalent) and be fully registered with the GMC, have
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. Additional Qualifications Special Instructions Please email Kara your CV, a short description of your research interests, and contact information for 3 references at kara_mckinley@harvard.edu . Selected
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team members to examine patients’ responses by analysing EEG and behavioural data. Candidates are expected to hold a medical qualification (MBBS or equivalent) and be fully registered with the GMC, have
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will work across four Work Packages, developing partnerships with schools, project partners and other groups, and using mixed methods to collect and analyse new data and synthesize existing data sets
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estimations in humanitarian and public health contexts by developing reproducible, multilingual workflows for social media analysis, building data pipelines in R/Python, and creating open-source tools for text