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Segment Care image labels; shape active-learning loops and QC. Productionize models with PyTorch, Docker/Kubernetes, and AWS/SageMaker Prepares manuscripts for publications. Ensure all members of the lab
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-learning loops and QC. Productionize models with PyTorch, Docker/Kubernetes, and AWS/SageMaker Prepares manuscripts for publications. Ensure all members of the lab are in compliance with the necessary
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Movement Sciences at Rutgers University is seeking a highly motivated Post Doctoral Associate to work on translational projects at the intersection of biomechanics, machine learning, exergaming, and mobile
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meteorology, human ecology, entomology, and environmental economics. In addition to instruction in the classroom, field, and laboratory, our students have opportunities for experiential learning on and off
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meteorology, human ecology, entomology, and environmental economics. In addition to instruction in the classroom, field, and laboratory, our students have opportunities for experiential learning on and off
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learning on and off campus, including a growing emphasis on international experiences through short- and long-term study abroad. Students also have access to a robust program of applied research and outreach
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well as leading efforts in mutual learning among researchers, practitioners, advocates, community organizers, and policymakers. The Associate will conduct research in the context of research practice partnerships
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environment. Highly motivated, passionate about science, a strong communicator, and possess an internal drive to learn independently. Equipment Utilized Physical Demands and Work Environment Overview While New
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analyses or the ability to effectively learn these techniques will be needed. Effective oral and written communication skills. Preferred Qualifications Experience in immunology, T cell biology, and cancer
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sciences and meteorology, human ecology, entomology, and environmental economics. In addition to instruction in the classroom, field, and laboratory, our students have opportunities for experiential learning