36 phd-mathematical-modelling-population-modelling Postdoctoral positions at The University of Arizona
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Genomics. The successful candidate will conduct research in population genomics, comparative genomics, and phylogenomics, with a focus on wildlife species of conservation concern. This role offers
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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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learning models and explore deep learning-empower solutions to securing next-generation cyber systems. This is a year-to-year appointment, contingent upon funding and performance. Outstanding UA benefits
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Postdoctoral Research Associates. The ideal candidates will possess a doctoral degree (PhD or MD) and have a strong background in molecular biology and physiology. There are three major projects in the lab: 1
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model, web maps, and geovisualizations for Cosmic Explorer that assists scientist’s in spatial decision making. Throughout your postdoctoral journey with us, you will be encouraged to conduct your own
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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