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defined differentiation protocols to generate diverse cardiovascular cell types from iPSCs, enabling advanced cardiovascular disease modeling and cell therapy applications (Nature Cardiovascular Research
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Research Associate I - III, Sarver Heart Center Posting Number req26948 Department Sarver Heart Center Department Website Link https://heart.arizona.edu/ Location Tucson Campus Address Tucson, AZ USA
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, linear mixed models, structural equation modeling, multilevel models) You are proficient in using one or more common statistical analysis software packages (R, SPSS, Mplus, Python, etc.) You have a very
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infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), particle sizing, and zeta potential. Knowledge of aquaculture, veterinary drugs, and Portuguese communication skills will be valued. How
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recruited into the WISE program will be a part of the WISE Research School https://wise-materials.org/research-school/ an ambitious nationwide program of seminars, courses, research visits, and other
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differentiation. The position will focus on designing and executing experiments involving Cas9 based perturbation, single cell multi omics method development, and downstream verification in transgenic cell lines
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differentiation of pluripotent stem cells into retinal cells and tissues. The work will specifically employ scalable culture modalities and bioreactors. This work will be undertaken in close collaboration with
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, engineering, physical or life sciences. ● Demonstrated experience with laboratory experiments (design, conduct, analysis). ● Demonstrated experience in modeling (formulating equations, and computing predictions
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is part of the WashU Cilia group and investigates motile cilia function using human and mouse airway models. We are interested in cilia biology, airway cell differentiation, motile ciliogenesis, and
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. Proficiency in R and/or Python and experience working in Unix/Linux computational environments. Familiarity with statistical methods for genomic data analysis, including differential expression, clustering