52 computing-"https:"-"IDAEA-CSIC" "https:" Postdoctoral positions at Cornell University
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prediction Risk assessment and catastrophe modeling High-performance computing and large dataset manipulation The position involves close collaboration with industry partners in climate risk assessment
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in conducting experimental, computational, and comparative work to understand how changes in gene expression underlie insect diet specialization and sequestration. They will be expected to come to
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Jacobs, will join the cadre of trainees at the Center including those supported by the NIH T32 AIPrN training program. The program is designed to train the next generation of scientists and build a
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formative and summative assessments of systems-thinking skills at the course and program levels; support scaffolding systems-thinking pedagogy across the E&S curriculum. The position is supported by Cornell’s
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Position Description Academic Job Description Department of Horticulture Postdoctoral Associate – Seed Biology and Technology Program The College of Agriculture and Life Sciences (CALS) is a pioneer
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developing this program. We are also particularly interested in strengthening the group working the CMS Track Trigger or Forward Pixel Detector for the HL-LHC upgrades. A PhD in experimental high energy
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this by producing scientific, computational, and big data resources that transform how we understand and protect the world around us. CESN engages the public by gathering biological information at large
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include designing and executing experiments to interrogate host-microbe interactions, analyzing and interpreting microbiome sequencing and/or metabolomics data, developing novel computational or analytical
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this by producing scientific, computational, and big data resources that transform how we understand and protect the world around us. CESN engages the public by gathering biological information at large
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Engineering Education, STEM Education, Engineering, Computer Science, or a related field. Demonstrated expertise in quantitative, qualitative, or mixed-methods research methods. Experience with human subjects