79 software-engineering-model-driven-engineering-phd-position Postdoctoral positions at University of Minnesota in United States
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Regular/Temporary Regular Job Code 9546 Employee Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job A PhD to work in Dr. Selmecki's lab in the Department of Microbiology and
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Arrangement: • The position is expected to work primarily on-site. Your work location will on the 3rd floor, Wallin Biomedical Biosciences. • The Department retains the right to modify flexible work arrangement
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familiarity with cryoem data processing using both Relion and cryoSPARC. Qualifications Required Qualifications: PhD in Biochemistry or related field. Extensive experience with cryoelectron microscopy sample
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and their application in animal models. There will be opportunities to lead a team of students, contribute to grant writing, engage in professional development, and disseminate results at conferences
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Regular Job Code 9546 Employee Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job A funded postdoctoral position with an emphasis on studying the synaptic mechanisms of cognitive
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Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job Postdoctoral Positions in somatic mosaicism in Aging and Longevity The Lei Zhang and Xiao Dong Laboratories at the University
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, equitable, and sustainable policy decisions. Qualifications Required Qualifications ● PhD degree in decision science, health services research, operations research, industrial engineering, applied
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to three full-time Post-Doctoral Associate (9546 Post-Doctoral Associate) positions. For more info on the division, visit http://www.sph.umn.edu/academics/divisions/biostatistics/. The Post-Doc will work
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heart failure are preferred. The employer retains the right to change or assign other duties to this position. About the Lab The position is in the laboratory of Dr. Aleksandra Babicheva with overall
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, and publication of major results from the experiment. They will also lead the development of predictive distribution models that incorporate data from the experiment. The project is funded by the USGS C