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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours ago
healthcare professionals and biomedical researchers from all backgrounds by facilitating learning within innovative and integrated curricula and team-oriented interprofessional education to ensure a highly
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and economy that respect people and their environment. We are looking for our next postdoctoral researcher in computer graphics and machine learning to join the Image, Data and Signal (IDS) department
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across learning sciences, computer science, machine learning, HCI and education research. Research Role Research themes for the NTO Postdoctoral Associate include, but are not limited to: Developing
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experience through research, internships, and industry-sponsored capstone projects. With a versatile curriculum spanning software, systems design, nanofabrication, and machine learning, the program prepares
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(payable 14 times per year) Responsibilities The applicant is expected to establish an own research group with focus on advanced machine learning and deep learning techniques for remote sensing applications
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or machine learning Experience with Swift development and/or strong willingness to work within the Apple development ecosystem Experience in edge AI, model optimization, or deployment of AI models
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and/or case-crossover analyses, and have a promising publication record. Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization
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. The applicant should be holder of a PhD in artificial intelligence/machine learning and possess excellent background in agentic systems, large language models and/or continual learning besides analytical
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and Immigration website . Full-Time, Fixed-Term (12 months) Applications are invited for the post of Postdoctoral Research Associate in Applied Machine Learning for High-Stakes Regulated Domains in
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position within a Research Infrastructure? No Offer Description Activities and context: The fellow will develop machine-learning interatomic potentials (MLPs), trained on density functional theory-DFT data