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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related
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localization. Completing data labeling tasks for machine learning models, including file and data format conversions. Annotating data, including but not limited to text transcription, classification, and object
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 4 days ago
include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
models (LLMs), Computer Vision and and Deep Learning architectures (CNNs, RNNs, etc). * Strong foundational knowledge in machine learning and data science methodologies. * Proficiency in programming, with
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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. Familiarity with artificial intelligence and machine learning concepts such as large language models, natural language processing, and Retrieval-Augmented Generation (RAG). Experience with AI/ML frameworks and
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Description REALISE - Bridging Igneous Petrology and Machine Learning for Science and Society About the REALISE Doctoral Network REALISE will train 15 Doctoral Candidates at the interface of igneous petrology
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simulation methods and quantum theoretical calculations in principle can address this but have hitherto struggled with tackling such challenging systems. With the emergence of machine learning methods in
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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participate in the development and implementation of Machine Learning algorithms for pattern recognition and automated simulation scenario generation. They will also contribute to the technical evaluation