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UKESM1 or similar models, advanced data analysis and machine learning, would be advantageous. Grade E: You will be near completion of a relevant PhD or have equivalent research experience, and be able
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a track record in computational modelling that explores the dynamics of AI systems and the development of autonomous AI agents, experience with machine learning, reinforcement learning, and generative
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use of molecular assays such as real-time PCR and next-generation sequencing to study the prevalence of infectious diseases in the target study populations. Learning about sample testing under a
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. Preferred Qualifications: Prior experience working with mouse models of cancer is strongly preferred; candidates without prior experience will be considered if willing to learn. Interest in tumor metabolism
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and energy materials. Preference will be given to those with knowledge of computer programming, AI or machining learning. Applicants are invited to contact Prof. Jianguo Lin at telephone number 2766
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: Genomics, precision medicine, bioengineering, and health data science AI and Digital: Machine learning, robotics, digital health, and cybersecurity Defence and Advanced Manufacturing: Secure systems
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partners, along with the databases we create. The geospatial smoke tradeoffs dataset can then be used in project planning. Learning Objectives: The fellow will develop professionally while engaging in
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, military service, pregnancy and pregnancy-related conditions, or other protected status. Create a Job Match for Similar Jobs About Harvard University Harvard University is devoted to excellence in teaching
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 2 months ago
both the NCRC in Kannapolis and UNC Charlotte in Charlotte. Experience in yeast genomics, RNA-sequencing, bioinformatics, or machine learning is preferred. Departmental Preferred Experience, Skills
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. In addition, the nature of the interaction between human and machine triggers new questions about the locus of agency and learning these emergent collaboration ecologies. Such examinations may require