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as well as have the opportunity to participate in future grant proposal development as part of the post doc mentoring plan. Application Materials A cover letter with a brief summary of research
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machine learning models, natural language processing (NLP), and ontology-based frameworks to enhance simulation, curriculum development, and personalized learning in health professions education. Develop
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
Position Title Post-Doc Research Associate Vacancy ID PDS004579 Full-time/Part-time Full-Time Temporary Hours per week 40 FTE 1 Work Location Chapel Hill, NC Position Location North Carolina, US Hiring Range
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The Robot Learning Lab in the Department of Computing at Imperial College London is seeking a talented Research Assistant (pre-doc) for an ambitious new project on dexterous robot learning. In
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to this position contact Eric Markvicka eric.markvicka@unl.edu Job Category Job Category (old) Post Docs & Other Academic Job Type Full-Time Temporary Position funded by grant or other form of temporary funding? Yes
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-electron Schrödinger equation for fermions and bosons with high accuracy and on the application of these methods to problems in the physics of oxides, semiconductors and their surfaces. Machine learning
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Acquire, process, integrate, and standardize large-scale multi-omics datasets and build databases Perform integrative and exploratory analyses of multi-omics datasets and apply machine learning methods
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standardize large-scale multi-omics datasets and build databases Perform integrative and exploratory analyses of multi-omics datasets and apply machine learning methods to uncover underlying biological
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Job Description Apply now Req Id: 37408 Job Title: Post Doc Research Associate City: West Lafayette Job Description: Job Summary A postdoctoral associate position is available in the Dept
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high