99 cloud-computing-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "St" Postdoctoral positions at Cornell University
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Systems Engineering (Health Systems Data Analysis, Modeling, Computing, and Cloud-Based Health Systems Analytics and Decision Support Platforms) as part of the CTECH Postdoctoral Fellows program. This
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undergraduate and graduate trainees as appropriate in the lab and field. This position will be funded by USDA NIFA Agricultural Microbiome Program. The individual will need to have the ability to drive to field
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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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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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of peer-reviewed publications in bioinformatics/multi-omics data. • Excellent written and oral communication skills. Preferred Qualifications: • Experience with cloud computing and high-performance clusters
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candidate is expected to engage actively in analysis of CMS data and will have considerable freedom in developing this program. We are also particularly interested in strengthening the group working the CMS
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research and training in social science fields related to transdisciplinary agroecology, such as critical agrarian studies, sociology, political ecology, human geography, STS, and development studies
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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 position will not supervise anyone. To apply: Please apply via Academic Jobs Online ( https://academicjobsonline.org/ajo/jobs/31628 https://academicjobsonline.org/ajo/jobs/31628 ">). Qualified candidates
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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