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development for DNA delivery; mRNA-based non-viral cancer immunogene therapy; and lipid nanoparticle platform development for inhalable mRNA delivery. The candidate will plan, design and execute experiments
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cloning, confocal microscopy, next-generation sequencing, bioinformatics. Mandatory requirements: Applicants must have obtained their PhD within the past seven years; demonstrate experience in at least one
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related fields.MD/PhD with molecular biology research experience. Experience/Training: Must have experience with analyzing omics data. Familiarity or direct experience with analysis of 10x Genomics data
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competition indicators. Experience of systematic evidence synthesis, meta-analysis or another form of structured, protocol-driven literature review. Competence in quantitative data handling and analysis using
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legislative processes. Experience of, or clear capacity for, elite interviewing. Desirable Criteria Reading competence in Mandarin sufficient to work with Chinese official documents. Experience with qualitative
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of Chemical and Biological Engineering Posting Number R260133 Posting Link https://www.ubjobs.buffalo.edu/postings/64055 Employer Research Foundation Position Type RF Professional Job Type Full-Time Appointment
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 16 days ago
Dependent on Experience Proposed Start Date Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and public service
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on understanding the impact of microbiota on neurodevelopment and stress and social behaviour using zebrafish as a model system. Experience of zebrafish as a model system is desirable. The ongoing projects utilize
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before the start of employment), very good experimental skills in modern imaging methods, microscopy, optical design, and optical measurements, 4 or more years of experience in the field, good programming
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-facing web GIS dashboard. Investigate the forest, landscape, and climate conditions that drive storm susceptibility, using major windstorms as natural experiments and interpretable machine-/deep-learning