66 phd-mathematical-modelling-ecological-modelling Postdoctoral research jobs at University of Minnesota
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induced pluripotent stem cells derived cardiac organoids for disease modeling and gene editing. This research aims to deepen our understanding of dystrophic cardiomyopathy and develop novel therapeutic
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-tuning DNA sequence/genomic/omic large language models (gLLMs) with their applications to genetics, e.g. in identifying causal genes for Alzheimer’s disease (AD). You will have access to state-of-the-art
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reports, grant writing, and extension and professional presentations and activities Qualifications Required Qualifications: - PhD in soil science, agronomy, environmental science or closely related field
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Department of Plant and Microbial Biology (PMB), led by Interim Head Dr. John Ward, comprises a diverse and integrative group of scientists who study the genetics, ecology, evolution, and molecular biology of
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(Including histone modification and DNA methylation) and 3D genome organization studies on the interplay between EBV infection and host interactions. Using in vitro B cell transformation model and 3D organoid
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skills in R programming - Working knowledge of Python - Experience with basic analyses to characterize gut microbiomes, including diversity analysis, differential abundance analysis, modeling microbial and
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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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include: -PhD (or equivalent terminal degree) in relevant discipline, in biological sciences, microbiology, chemical engineering, biomedical engineering, or mechanical engineering -Experience with funded
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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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with stress, but few other generalities have emerged, in part because, researchers across diverse fields—from cell biology to genomics, agriculture, cancer, and ecology—rarely interact. Through