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, bioinformatics, and functional microbiome studies conducted in field trials, greenhouse experiments, and laboratory settings. This role will focus on the interactions between rhizosphere microbiota and plant roots
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bioinformatics Communicating the results in scientific publications and conference presentation Co-supervising PhD and MSc students working in the project Contributing to new research ideas and (co)-applying
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experience. The above statements are intended to describe the general nature and level of work performed by people assigned to this classification. They are not intended to be construed as an exhaustive list
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-transmission electron microscopy. -Develop bioinformatics workflows for processing and analyze large data sets. -Perform key laboratory techniques such as PCR, molecular cloning, SDS-PAGE, and other basic
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will represent a gradient of Ne. In addition to standard bioinformatics work the project encompasses analysis of demographic history and Ne, estimation of inbreeding and genetic load, and eco
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, United States of America [map ] Appl Deadline: (posted 2025/09/04, listed until 2026/02/20) Position Description: Apply Position Description Postdoctoral Associate – Scientific Machine Learning for Multiscale Biological
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functional analysis of soil biology. Utilize high-throughput sequencing technologies to analyze soil microbiomes and interpret the data using bioinformatics tools. Collaborate with interdisciplinary teams
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will represent a gradient of Ne. In addition to standard bioinformatics work the project encompasses analysis of demographic history and Ne, estimation of inbreeding and genetic load, and eco
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. This list of duties and responsibilities is not intended to be all-inclusive and may be expanded to include other duties or responsibilities as necessary. CORE QUALIFICATIONS Education: Doctorate degree
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techniques Demonstrated ability to work independently and collaboratively. Proven publication record in peer-reviewed journals. Knowledge of bioinformatics tools for transcriptomic or proteomic data analysis