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will contribute to developing and evaluating state-of-the-art methods for predicting mental health outcomes from multi-modal clinical and digital health data. This position offers the opportunity to work
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Program, and the field site in Kenya, and engage with investigators at all sites across various disciplines. The project will focus on conceptualizing, innovating, and implementing data-driven approaches
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are expected. This position requires a PhD. To apply, candidates should submit a cover letter, curriculum vitae, and contact information for three references to: amy.schmid@duke.edu. Duke is an Equal Opportunity
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principle not be enough for this position. Fluency in data analysis in R, and strong experimental skills are essential. Experience with field observational studies, handling or large datasets and mathematical
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and immunoprecipitations RT-PCR and RNA analyses Cell transfections and selections RNA-seq and sequencing data analysis RNA modification profiling Lead one or more research projects investigating RNA
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, with a particular emphasis on Urban Resilience to Climate Risks. Current research themes include: • Adaptation of People: Leveraging big data and computational methods to analyze adaptation behaviors and
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for control and analysis of instruments, applying these systems to the study of human diseases, and acquiring and analyzing clinical data sets. Programming skills should include MATLAB, Labview, Python and/or C
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) to develop novel approaches for overcoming resistance in immunotherapy and for enhancing hematological and immunological recovery in radiation exposure; 2) to perform experiments and collect data, and to
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animal species, generating standardized data that works effectively across diverse languages and cultural contexts while eliminating traditional barriers of recall bias. These methods are being deployed in
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/bioinformatics, and data science. Work Performed · Work in highly collaborative inter-disciplinary environment with clinicians, econometricians, statisticians, and data scientists · Lead statistical analysis