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analysis and machine learning methods applied to protein structure determination using single-particle cryo-electron tomography (ET). The candidate will contribute to the design, development, and
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, Duke University Biology Department to study how archaeal microbial communities respond to stress in hypersaline environments. A PhD in computational and/or experimental biology is required in fields
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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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investigate the cell division cycle of Cryptococcus neoformans. This position requires a minimum of a PhD degree. Applicants should hold a PhD in Biology or a related field and have expertise in fungal genetics
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. Qualifications: Qualifications include a PhD or equivalent in environmental health, epidemiology, biostatistics, or a closely related discipline. The successful candidate should be highly organized and have
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are opportunities for collaboration. Please start the filename for your cover letter and CV with your last name. This position requires a minimum of a PhD degree. Duke is an Equal Opportunity Employer committed
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Duke University is inviting applications for a postdoctoral position in economics. Postdoctoral applicants are expected to have completed their PhD within the last two years, and the position would last
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to mentor students, teach/train other researchers in LCA tools, and develop independent research projects as desired. The successful applicant will possess a PhD in chemical engineering, chemistry
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regarding the use of lasers, chemicals, infectious agents, animals, and human subjects, as needed. Requirements: PhD Duke is an Equal Opportunity Employer committed to providing employment opportunity without
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for this position will be a highly motivated individual with experience in deep learning and medical imaging and a PhD degree in computer science, electrical and computer engineering, biomedical engineering