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Field
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engagement, FIU is redefining what it means to be a public research university. The Green Library is currently seeking Student Assistants to join their Learning and Research team of professionals Duties
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cancer research, with a particular focus on developing computational methods to analyze imaging and clinical data in patients with liver cancer Position Overview Machine learning and deep learning applied
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support for the lab Job Requirements: Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, or related field Knowledge and experience in world model, computer vision and deep learning
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, adapting or evaluating machine-learning or deep-learning models. You should also have experience of organising datasets, conducting computational experiments and interpreting quantitative results. Working
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imaging datasets, electrophysiology datasets or video data) is highly desirable. Proficiency in popular deep learning, computer vision and/or user-interface software frameworks (such as Pytorch, TensorFlow
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to the development of privacy-preserving indoor localization methods using multi-modal deep learning, the design of wearable multi-sensor navigation devices, and the creation of datasets for indoor trajectory
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to gain deep, publishable experience before a PhD or industrial R&D career. About the Person The successful candidate must have, and your application should clearly demonstrate that you meet the following
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Job Summary: The Ecological Approaches to Social Emotional Learning (EASEL) Lab, led by Professor Stephanie M. Jones, seeks a full-time Research Assistant to support our team in the Human Development
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& Responsibilities Support the PD Specialist in conducting needs assessments, environmental scans, as well as program designs for graduate students and postdocs. Participate in the planning and implementation of PD
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Job Summary: The Ecological Approaches to Social Emotional Learning (EASEL) Lab, led by Professor Stephanie M. Jones, seeks a full-time Research Assistant to support our team in the Human Development