16 data-visualization-analysis Postdoctoral positions at Case Western Reserve University
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under the direction of senior personnel. This includes carrying out advanced microbiome experiments using proper methodologies; assisting in data analysis; providing reports; presenting at lab meetings
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on qualifications, experience, department budgets, and industry data. Employees receive more than just a paycheck. University employees enjoy a comprehensive benefits package that includes excellent healthcare
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data analysis: genome wide association, whole genome sequencing, RNA-seq, epigenetics, proteomics, metabolomics, Alzheimer’s disease etc. • Excellent communication and writing skills. Interested
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conduct experiments aimed at understanding tumor-immune interactions in patient-derived glioblastoma organoids and other preclinical brain tumor models (30%). Data Integration and Analysis: Perform
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educational background. Experience with mass spectrometry data analysis in the life sciences. Ability to work independently and effectively in a collaborative team environment for solving scientific problems
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various individuals, including patients for study. 3. Previous experience in evidence based psychotherapy. 4. Writing and data analysis. 5. Ability to work collaboratively with the principle investigator
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or economics), epidemiology, biostatistics, statistics, data science, applied mathematics, or related field Other skills & competencies: Data management and cleaning and conducting descriptive analyses
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responsibilities of the postdoctoral scholar are: 1) Using several existing long-term data sets, conduct analyses and prepare manuscripts about amphibian population dynamics, movement and phenology. 2) Take a
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of work. Record, analyze, and present data on all work relevant to the project. Work with faculty and research personnel to prepare reports on the progress of the program. Mentor and monitor graduate
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, including collaborations with multidisciplinary teams. The qualified candidate will prepare manuscripts and contribute preliminary data, background, significance, and experimental design for grant proposals