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Field
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computer science knowledge. Preferred Knowledge, Skills, and Abilities: Practical experience developing novel AI/ML algorithms and models. Knowledge about hardware architectures, compilers, neural network
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by publication record). Excellent programming and computer science skills. Preferred Knowledge, Skills, and Abilities: Practical experience developing novel ML and NLP algorithms and models and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
looking for a postdoctoral fellow interested in developing either machine learning algorithms for high-resolution histopathology imaging/spatial-profiling data in combination with other modalities (e.g
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of the following: Ecosystem Modeling, Machine Learning, Microbiome, Microbial Ecology, Soil Science, or Computational Biology. The positions are for several different projects, including the following: (P1
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at intersections through real-time detection, classification, and tracking of vulnerable road users (VRUs) using advanced sensing technologies and artificial intelligence algorithms. The appointment is tentatively
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professional career afterward. The Radiation Oncology department has a CAMPEP approved Medical Physics certificate program. The postdoc fellow will have the option to enroll in the certificate program with
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excellent opportunity to work in the development of external relationships and to engage in industry-university partnerships at the cutting edge of engineering, computer and data science, technology, natural
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and uses these applications to inform new foundational ML and NLP innovations. The position provides unique access to world-class computing resources, such as the BNL Institutional Cluster and DOE
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learning field. Excellent programming and computer science skills. Preferred Knowledge, Skills, And Abilities Practical experience developing novel ML, LLM, or CV algorithms and models. Experience with state
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collection, and data analysis. Work activities may include the following. • Developing analysis tools to extract physical meaning from in-situ process data. • Developing machine learning algorithms to drive