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Field
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and multi-omic datasets; integrating genomic findings with clinical phenotypes; and supporting functional studies to evaluate relevant genes and variants associated with chromosome 9p disorders. The
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computational component and will include advanced deep learning methods for image analysis and genomic data integration to identify molecular programs associated with different functional cell states
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editing materials, or functional genomics resources. Experience with soybean seed composition analysis and interpretation of seed quality data. Knowledge of soybean breeding methods, field trial design
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, metabolic pathways, functional traits or community-level processes in an ecological or evolutionary context. You have experience with ancient or degraded DNA analysis, including authentication and the
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, opto/chemogenetics, in vivo calcium imaging, and histology. Performs functional and phenotyping analysis in living animals. Prepares samples and perform validation studies for omics analysis
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the direct mentorship of Dr. Ghosal and co-PI Dr. Cui, the fellow will lead the development and execution of advanced functional and distributional data analysis techniques. This role requires
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independently and motivated to contribute to the wider research mission of the lab Desirable Qualifications: • Graduate experience with neuroimaging analysis (e.g., fMRI, structural MRI, functional
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collaborations on Campus such as within the Collaborative Research Center CRC1690 (Disease Mechanisms and Functional Restoration of Sensory and Motor Systems, https://sfb1690.uni-goettingen.de ) and
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databases, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a
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to organismal phenotypes through mouse models, auditory physiology, molecular analysis, and experimental manipulation of the inner ear. Current and developing projects include functional