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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | 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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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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record of computer programming. Experience in numerical relativity and/or numerical simulation of quantum field-detector interaction Strong written and oral communication skills. Demonstrated ability
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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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consequences to sensitive species. Revisions also include updating values in the existing models that reflect new science and lessons learned over the last 15-years. The toolkit also includes evaluation tools
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models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in machine learning, computer vision, and medical
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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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, 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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. 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
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postdoctoral fellow, committed to advancing inclusive and interdisciplinary science, to join an international team applying state-of-the-art machine learning technologies to stem cell and immune engineering in