60 design-"https:"-"https:"-"https:"-"Vilnius-University" Fellowship positions at Zintellect
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Objectives: Throughout the appointment, you will learn training in design of experiments, preparation of scientific manuscripts and reports, presentation of data at scientific meetings, analytical assay
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will be able to engage with a multi-disciplinary team and may help in designing and developing visualizations and dashboards. Results of the research may inform updates to data considerations
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expected to learn both independently and collaboratively within a multidisciplinary research team, contribute to experimental design and data analysis, publish findings in peer-reviewed journals, and
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the development of follicular helper T (Tfh) cells and germinal center B cells. Learning Objectives: You will participate in experimental design, immunization and analyzing cellular and molecular responses
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bioinformatics software). Strong foundation in experimental design, advanced statistical analysis, and scientific writing. Proven research experience in insect biology, ecology, or biological control, with a clear
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veterinary importance. Under the guidance of the mentor(s), the fellow will contribute to the design, implementation, and data analysis of field-based research on new world screwworm and mosquito-borne
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Discipline(s): Chemistry and Materials Sciences (12 ) Communications and Graphics Design (2 ) Computer, Information, and Data Sciences (17 ) Earth and Geosciences (21 ) Engineering (29
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, data collection, analysis, and QA/QC for existing measurements as well as development of data collection and analysis procedures for a suite of new instruments. You will also gain experience in designing
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in the development of diagnostic technologies for viral, bacterial and fungal diseases. Functional knowledge of molecular techniques such as: PCR; real-time PCR; PCR primer/probe design and DNA
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the ability to design, implement, and document Bayesian and machine learning models to predict abundance, prevalence, and disease risk, demonstrating understanding of model selection, assumptions, and