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://aria.org.uk/opportunity-spaces/resilient-climate-and-ecosystems/accelerated-adaptation for more information on ARIA and this programme). The project is a collaboration between groups at the Universities
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publication record and experience analyzing large-scale genomic datasets in HPC environments. Applicants must be within 5 years post receipt of their PhD. Terms of employment include a competitive salary and
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the research group and will work with collaborators at other institutions (nationally and internationally) The successful candidate should have the following qualifications: PhD in Epidemiology, Data Science
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internationally) Requirements: The successful candidate should have the following qualifications: • PhD in Epidemiology, Data Science, Applied Mathematics, Computer Science, Statistics, Public Health, Economics or
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/opportunity-spaces/resilient-climate-and-ecosystems/accelerated-adaptation for more information on ARIA and this programme). The project is a collaboration between groups at the Universities of York and Exeter
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depth in some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs
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Required: PhD in Biomedical Engineering, Bioengineering, Computer Science, Biophysics, Computational Biology, Molecular Biology, Genomics, or a related field Proficiency in computational data analysis and
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
Description The Center of Interdisciplinary Data Science and Artificial Intelligence (CIDSAI) at NYU Abu Dhabi seeks to recruit a highly motivated Post-Doctoral Associate to work on topics in
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Science, or Information Science, with no more than five years post receipt of the PhD. The position requires experience with at least one of the following: Data Science, Machine Learning, Computational Social Science, Big Data
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health