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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 13 hours ago
(Col) Work Unit Epidemiology and Comm Health Work Location Vacancy Open To All Candidates Position Designation Post Doc Employment Type Temporary - Full-time Hours per week 40 Work Schedule Pay Rate
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 13 hours ago
& Information Systems Work Location Woodward Hall Vacancy Open To All Candidates Position Designation Post Doc Employment Type Temporary - Full-time Hours per week 40 Work Schedule 8:00 am-5:00 pm, Monday-Friday
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of AI and in particular machine learning (ML). As today’s mainstream AI/ML workloads often resort to large-scale and energy-hungry supercomputers, it is necessary have a more critical look at how HPC
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design and in silico validation intimately connected to experimental validation. In this project, you will develop machine learning methods and apply them in an interdisciplinary environment spanning
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of the post. Candidates without a master’s degree have until 1st of July 2026 to complete the final exam. Strong programming and artificial intelligence/machine learning skills. The candidate’s research
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of immune cell function. These projects are focused on making safer and more effective cell therapies (e.g., CAR-T) and gene therapies for cancer and beyond. We are an interdisciplinary lab spanning
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doctoral and research posts at UiO (Norwegian) and Regulations for the degree of Philosophiae Doctor (PhD) at the University of Oslo . The University of Oslo has a transfer agreement with all employees
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kind of machine learning algorithm, provides more accurate data than traditional data collection methods, e.g. paper-based surveys. This data is valuable to several stakeholders: i) architects and urban
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laboratory analytical methods (e.g., chromatography, mass spectrometry). Familiarity with AI or machine learning applications relevant to environmental data analysis. Basic knowledge of GIS/mapping tools
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. Demonstrated experience applying machine learning and AI-based approaches to empirical disease, ecological, or biological datasets, with an emphasis on pattern identification, prediction, or spatial risk mapping