85 software-engineering-model-driven-engineering-phd-position Postdoctoral positions at University of Minnesota
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. The candidate must have a track record of reliability and good verbal and written communication skills. This is an ideal position for a recent graduate with an PhD, MD/PhD degree in related fields, but not
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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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Regular/Temporary Regular Job Code 9546 Employee Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job Position Summary: This post-doc will conduct research broadly in the area of
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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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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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Code 9546 Employee Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job About the Job: 30% Lead research projects pertaining to development of large animal models of disease
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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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in science and engineering, chemistry is critical for solving society's most important problems and making significant positive impacts on human health, energy, and the environment. The UMN Department
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an independent research project and lead a team of undergraduate/postgraduate researchers. Candidates should be able to manipulate and engineer yeast, using classical yeast genetics and modern CRISPR-cas9
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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