15 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Saint Louis University
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Fellow with a PhD in Computational Biology, Bioinformatics, Biostatistics, Data Science, or a related quantitative field to join an interdisciplinary research program focused on Alzheimer’s disease (AD
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management, and administration. Qualifications: Self-motivation and working independently. Data analysis skills. Trouble shooting ability. Excellent written and verbal communication skills. PhD in Virology
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research agenda. The ideal candidate will have experience conducting program evaluations and designing research projects related to education policy issues. NIH funded postdoctoral positions are available
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a team-oriented research environment. PREFERRED QUALIFICATIONS Prior experience in one or more of the following areas: longitudinal data analysis, survival analysis, spatial methods, machine learning
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analyze experimental data using statistical and computational tools. Maintain accurate records of research procedures and results; prepare reports and manuscripts for publication. Utilize electronic data
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. Qualifications A recent PhD in Plant/microbial Biology, Biochemistry, or related discipline Relevant Experiences in Bioinformatics and Proteomics Experience in basic Microbiology techniques including tissue
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status, sexual orientation, military/veteran status, gender identity, or other non-merit factors. If accommodations are needed for completing the application and/or with the interviewing process, please
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applied for without regard to race, color, religion, sex, age, national origin, disability, marital status, sexual orientation, military/veteran status, gender identity, or other non-merit factors. We
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, and publication. This position involves both wet-lab and computational work. The fellow will apply cutting-edge genomics technologies to collaborative projects and manage the day-to-day operations of
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for the position applied for without regard to race, color, religion, sex, age, national origin, disability, marital status, sexual orientation, military/veteran status, gender identity, or other non-merit factors