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Field
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, preparing manuscripts, drafting and submitting competitive internal and external grants Perform other duties as assigned Requirements A Ph.D. degree with a strong research background in bioinformatics
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) · Histology, immunofluorescence, and biomarker quantification · Translational science platforms including organ-on-chip, bioreactor systems, and bioinformatics workflows for high-dimensional data integration
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 26 days ago
chemistry, statistics, bioinformatics, geography, or epidemiology for an NIEHS funded T32 postdoctoral fellowship in Environmental Science and Engineering. The initial appointment period is 1 year with a
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immunosorbent assay (ELISA) analyses to measure intestinal health. Analyze soil and fecal microbiome sequencing data using established bioinformatics pipelines. Administrative & Data Management Mentor graduate
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. The project forms part of the multi-disciplinary SoilSystems Priority Program, involving 16 collaborating research institutions across Germany. Your tasks: Bioinformatic analysis of long- and short-read
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degree in molecular biology, plant genetics, plant cell biology or related topics. Proven expertise in plant pathology and plant-microbe interactions is most desirable, and knowledge of bioinformatics
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in computational biology, biomedical engineering, bioinformatics, computer science, statistics, or a related field. Strong background in machine learning, data integration, and high-dimensional data
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MD/PhD with a strong background in virology or other areas relevant to our research. List of preferred technical skills (not all necessary): Bioinformatics analysis (RNA-Seq analysis) Cell culture
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culture, expertise in phylogenetic analysis of large NGS datasets, and proficiency with relevant bioinformatic pipelines, statistical tools, and software is required. This experience could include graduate
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Computer Science, Biomedical Engineering, Bioinformatics, or a related field. Required: Strong understanding of machine learning (ML) and deep learning (DL) methods, with hands-on experience in model development and