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Field
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understanding the intricacies of plant chemistry and biology. Research in the Nett lab spans multiple, distinct projects that are all unified by the chemistry of plants, including: 1) evolutionary and biochemical
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field. Our work will be at the intersection of stem cell, developmental, and evolutionary biology, so we are particularly interested in candidates with a strong research background in: -Embryonic stem
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transcriptomic data and an interest in evolutionary biology and conservation are highly desirable. Candidates must hold a PhD (or equivalent) in conservation genomics, evolutionary biology, or a closely related
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. The appointee for this position will study the ecological and evolutionary processes that drive the rise and fall of antibiotic resistance in bacterial communities. The appointee will be conducting experiments
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the biological sciences and experience in molecular genetic techniques, including high throughput sequencing and data analysis. Strong research background with comparative evolutionary questions, insect herbivores
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REQUIREMENTS The College of Liberal Arts is seeking part-time research support. The ideal candidate will assist with data collection and analysis for a project about the evolutionary biomechanics
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-evolutionary biology -ecological genetics -metabolomics -proteomics -cell signaling -bacterial/yeast/animal cell molecular biology and genetics Expected Salary Range: Salary begins at $65,000/year and will be
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, Insect Ecology, Evolutionary Biology, Molecular Biology, Genetics, Bioinformatics, or a closely related field). Degree must have been received within the past five years, or be pursuing and anticipated
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in the biological sciences and experience in molecular genetic techniques, including high throughput sequencing and data analysis. Strong research background with comparative evolutionary questions
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. Applicants may come from a wide range of relevant disciplines including genomics, ecology, evolutionary biology, microbiology, bioinformatics, environmental science, biotechnology, computational biology