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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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collaboration between Dr. Warren Grill and Dr. Angel Peterchev at Duke University and Dr. Axel Thielscher at the Danish Research Center for Magnetic Resonance and Technical University of Denmark. This work is
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for Magnetic Resonance and Technical University of Denmark. This work is part of a long-standing research program at Duke for rigorous computational modeling and experimental characterization of both invasive
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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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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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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