139 machine-learning-"https:"-"https:"-"https:"-"https:"-"UCL"-"UCL" positions at University of Kansas
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machine learning methods. Provide theoretical predictions to guide experiments, and atomic-scale physical understanding to experimental observations. Publishing findings in peer-reviewed journals
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, interventions, and practices into engaging digital and print learning experiences. In this role, you will design online courses, training modules, videos, graphics, and assessments, as well as printed materials
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materials. Excellent computer skills, including familiarity with scheduling software (Outlook), Microsoft Office (Word, PowerPoint, Excel) and ability to learn other software as stated in application
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. This support will include: Support of computer equipment utilized by campus stakeholders in classrooms, labs, offices, and meeting spaces. As required, general support of AV systems utilized by the TSC in a mix
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through these interactions. This support will include: Support of computer equipment utilized by campus stakeholders in classrooms, labs, offices, and meeting spaces. As required, general support of AV
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practitioners, and socially engaged leaders, in an ever-changing professional field. Design students have access to studio spaces, computer labs, a letterpress lab, a Riso lab, a robust photography area, and
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. The coordinator partners across all Student Affairs departments to advance a culture of assessment and evidence with the goal of enhancing programmatic/operational outcomes and student learning in alignment with
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student learning in alignment with strategic priorities. The individual will enhance Student Affairs’ effectiveness by coordinating, developing, implementing, and managing assessment, evaluation, and
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Sensing and Integrated Systems (CReSIS), and the KU Medical Center. The EECS department offers undergraduate and graduate degrees in electrical engineering, computer engineering, computer
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supersonic and hypersonic flows, as demonstrated by application materials. Familiarity with machine learning or data-driven modeling approaches in fluid dynamics, as demonstrated by application materials