70 phd-mathematical-modelling-ecological-modelling Postdoctoral positions at University of Minnesota
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Previous Job Job Title Dunham-Jackson Postdoctoral Associate - School of Mathematics Next Job Apply for Job Job ID 370276 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular
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, working groups Qualifications Required Qualifications: ● PhD in water resources, hydrology, aquatic ecology, limnology, wetland ecology or a related field ● Experience with synthesis and analysis of large
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Preferred Qualifications : Competitive candidates should be highly motivated and possess a PhD in quantitative/spatial ecology of wildlife or a related discipline with a strong quantitative emphasis (ABD
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metabolism in model systems. ● 15% Publication and presentation of data → The postdoctoral scholar is expected to communicate their research findings through publication in peer-reviewed research journals and
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inflammation can promote atrial electrical, Ca2+-handling and structural remodeling that initiates AF in a model of metabolic syndrome. You will be expected to use mouse models to be both mechanistic and
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Qualifications Essential Qualifications • PhD in mathematics, science or STEM education research or equivalent (e.g., PhD in biological field with dissertation on discipline-based education research) • Experience
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multiphase flow in porous media. 80% - Applying numerical and analytical infiltration models to quantify groundwater recharge potential under varying hydrogeologic conditions. In parallel, the researcher will
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disease which combines behavioral and genetic models, metabolic, neuroendocrine and imaging techniques in living animals as well as sophisticated molecular and structural biology analyses of neuropeptides
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are subject to change as research needs change. It is expected that up to 20% of job duties may change annually. Qualifications Qualifications: PhD degree required in ecology, plant or microbial biology
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, and publication of major results from the experiment. They will also lead the development of predictive distribution models that incorporate data from the experiment. The project is funded by the USGS C