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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 2 months ago
for Energy and Environmental Policy Research (CEEPR) is seeking a motivated post-doctoral associate to work on the mining and processing of minerals related to the energy transition. The Postdoctoral Associate
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data issues. Utilizing machine learning techniques as appropriate for data analysis. Developing computing programs and software to support research initiatives. Applying new methodologies to real-world
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about the ILR School can be obtained at our web site, http://www.ilr.cornell.edu and CAROW at https://www.ilr.cornell.edu/carow . Requirements: Applicant must have a PhD in labor relations, sociology
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: • Responsibilities include data analysis, development of software tools, and preparation of manuscripts. • Other duties as assigned. Qualifications Required: • PhD in a STEM-related field • Strong quantitative and
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: Ability to work collaboratively with others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications. Communication
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neuropsychological assessment with brain imaging data (e.g., functional MRI, structural MRI, EEG) Proficiency with neuroimaging analysis software or electrophysiological data processing Experience conducting clinical
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PhD with an interest in computational social science or data science (broadly defined). This individual will help design, execute, and analyze a series of field experiments on a platform created by the
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, document, and release production-quality software packages Department Specific Qualifications Education: PhD in Bioinformatics, Computational Biology, or Computer Science with biological applications
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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting