302 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" uni jobs at Nature Careers in United States
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research. Initially, jET interns will be dedicated to learning engineering tools and best practices by assisting staff engineers and scientists in ongoing work. Later on, jET interns will be expected to work
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, military branch, conflict counselling). Experience in data entry and working in emergencies and fast pace, stressful environment. Some experience with computer systems, including Microsoft Office (Word
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positive and collaborative working environment. Foster innovation in teaching and learning by supporting faculty in pedagogical advancements and instructional technologies. Student Engagement and Success
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diseases, using primary hematopoietic graft products and genetically modified products for sickle cell disease and CAR T-Cell therapy applications. The Human Applications Laboratory is composed of two
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to integrating computational simulation, data science, and deep learning technologies to deeply explore structure–property relationships in materials. Its goal is to drive the precise design and development of new
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and services by utilizing the computerized scheduling system in an accurate, efficient manner. Maintains scheduling (clinic-specific) information and computer knowledge to ensure safe and effective
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collecting relevant data from 2D, 3D or 4D images. Perform computer automated analysis and quality control on large data sets. Liaise effectively with other groups at Janelia to manage multiple image analysis
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, protected veteran status, military service, genetic information, sex, sexual orientation, or pregnancy. Questions or concerns about the application of Title IX, which prohibits discrimination on the basis
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for developing and implementing new informatics tools and resources to enhance phenotyping performance or enable deep phenotyping through terminology/ontology, natural language processing, and machine learning
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to integrating computational simulation, data science, and deep learning technologies to deeply explore structure–property relationships in materials. Its goal is to drive the precise design and development of new