75 assistant-professor-and-data-visualization Postdoctoral positions at Duke University
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analyzing single-cell and spatial profiling data using modern techniques. This work should have contributed to significant pre-prints or published manuscripts, where the candidate played a leading role
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to providing employment opportunity without regard to an individual's age, color, disability, gender, gender expression, gender identity, genetic information, national origin, race, religion, sex (including
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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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innovative statistical methods in clinical trial design and variable selection methods in high dimensional data that will predict clinical outcomes and meta-analyses. The successful candidate will collaborate
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will join the STRONG Manoomin Collective and work with Dr. Kim Marion Suiseeya. The Collective supports Indigenous-led, data-driven resilience strategies through sovereignty-affirming and culturally
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, United States of America [map ] Subject Areas: Computer Programming Systems Modeling Biochemistry Atmospheric Science Appl Deadline: (posted 2025/02/19, listed until 2025/05/01) Position Description: Apply Position
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,genderexpression,genderidentity, genetic information, national origin, race, religion, sex, sexual orientation, or veteran status. Duke aspires to create a community built on collaboration, innovation, creativity
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contact information of three professional references. Duke University is an Affirmative Action/Equal Opportunity Employer committed to providing employment opportunity without regard to an individual's age
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committed to providing employment opportunity without regard to an individual's age, color, disability, gender, gender expression, gender identity, genetic information, national origin, race, religion, sex
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and galaxy clustering calibration and measurement pipeline ahead of the Roman launch in late 2026, while taking advantage of early LSST data and existing Dark Energy Survey data for science analyses