31 phd-mathematical-modelling-ecological-modelling Postdoctoral positions at The University of Arizona
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Position Highlights We seek an early career scientist with background in ecohydrology, numerical modeling and/or remote sensing to fill a position as Postdoctoral Research Associate at the Department
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USA Position Highlights We seek an early career scientist with background in ecohydrology, numerical modeling and/or remote sensing to fill a position as Postdoctoral Research Associate
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Number req22961 Department Ecology & Evolutionary Biology Department Website Link Location Main Campus Address Tucson, AZ USA Position Highlights The McNew Lab at the University of Arizona seeks a
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undergraduate students). Additional duties as assigned. Minimum Qualifications Must have completed all the requirements for a PhD. in physical science, mathematics/ data science, or engineering at the time of
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patient-specific human induced pluripotent stem cells (iPSCs), primary human cells/tissues, along with animal models, to develop a platform for the evaluation of cardiovascular toxicity associated with
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use a combination of patient-specific human induced pluripotent stem cells (iPSCs), primary human cells/tissues, along with animal models, to develop a platform for the evaluation of cardiovascular
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models, human tissue and blood samples. Use biochemical, cellular, molecular, and pharmacological approaches to for mechanistic studies. Use cutting-edge technologies for transcriptomic, proteomic, and
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progression and metastasis, and the combination of radiation treatment with immune checkpoint inhibitors in anti-cancer therapy. The positions will involve the use of animal models, human cell lines, and
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activities as assigned. Minimum Qualifications The minimum qualification for this position is a PhD. The selected candidate must have a conferred PhD upon hire. Preferred Qualifications FLSA Exempt Full Time
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. Contribute to grant writing. Minimum Qualifications PhD in computer science, computational biology, or related field. Dissertation must have focused on development of neural networks for analysis of proteins