93 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "University of Plymouth" Postdoctoral positions at Cornell University
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for dissolved organic matter. ● Integrate sensor outputs into process control systems (stepper motor–driven coagulant dosing). Desired qualifications: ● PhD in Environmental Engineering, Electrical/Computer
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science research and perform data analysis. 15% Publication and Presentation. Lead and/or contribute to peer-reviewed publications and conference and public presentations to share research findings. 10
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physics, electronic structure theory, hydrodynamics, machine learning, materials science, statistical physics, strongly correlated electrons, quantum information. While this fellowship targets theorists
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experience with species distribution modeling, climate data analysis, and natural resource decision-making. The successful candidate will use data from eBird and structured monitoring programs to identify
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and present our work at top-tier ACM computer science venues in Human Computer Interaction (HCI) and Wearable Computing. Example projects and publications from our group: Intro to the Hybrid Body Lab
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"Advancing the Study of Social Isolation, Loneliness, and Cognitive Functioning in Older Adults Through the Use of Integrative Data Analysis.” As a postdoctoral associate, you will focus primarily on data
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: Please apply via Academic Jobs Online (https://academicjobsonline.org/ajo/jobs/29926 ), Qualified candidates should submit a short cover letter, curriculum vitae, contact information for three references
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staff. To apply: Please apply via Academic Jobs Online (https://academicjobsonline.org/ajo/jobs/30026 ). Qualified candidates should submit a short cover letter, curriculum vitae, contact information
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on the CMS experiment at the CERN LHC . The Cornell CMS group has a broad involvement in many aspects of the experiment, from data analysis to detector operations and high luminosity detector upgrades (HL
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existing national datasets (including remotely sensed data) and will conduct a mixed-methods data collection campaign that emphasizes mapping intervention-specific adoption, adoption outcomes and identifying