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information of 3-5 references and copy of research statement. Duke is an Equal Opportunity Employer committed to providing employment opportunity without regard to an individual's age, color, disability, gender
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mechanisms underlying risk for addictive disorders—with a primary focus on tobacco dependence and relapse—and translating this information to develop more efficacious interventions. This position is supported
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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and
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Equal Opportunity Employer committed to providing employment opportunity without regard to an individual's age, color, disability, gender, gender expression, gender identity, genetic information, national
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, including Computer Science, Electrical and Computer Engineering and Music and will be expected to participate in the preparation of research proposals and scientific publications. The candidate will also be
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experimental procedures. • Data preparation for oral presentations, grant applications and publication in professional journals. • Implementation of bioinformatics approaches. • Conformance to standards
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statement outlining potential mentors and research programs from the list of faculty mentors. More information about the program and the list of faculty mentors can be viewed at: https://medicine.duke.edu
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experimental procedures. Data preparation for oral presentations, grant applications and publication in professional journals. Implementation of bioinformatics approaches. Conformance to standards of responsible
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27710, United States of America [map ] Subject Areas: Statistics / Statistics Biostatistics / Biostatistics and Data Science Data Science / Machine Learning Appl Deadline: none (posted 2025/02/12
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publish results in peer-reviewed journals. Training in bioethics, laboratory management, and scientific writing. Maintain accurate laboratory documentation of experiments, including raw experimental data