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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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cardiovascular treatments. In this role you will apply both machine learning predictive modelling and human genetic analyses of non-coding regulatory sequences to identify cell-specific targets for coronary artery
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
Experience with Knowledge Graph machine learning including KG embeddings. Strong background in data integration, ETL pipeline design, and schema transformation. Familiarity with workflow managers, with a
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of state voting legislation for the Voting Laws Roundup. This work includes developing computational tools (e.g., using large language models, machine learning for text analysis and classification, etc
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networks; experience in applying machine learning models and processing imagery from UAS and satellite platforms. Other Requirements: Willingness to work irregular hours and in occasionally adverse weather
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a position combining project management, machine-learning model development, data management/analysis, and manuscript writing for publication. Preference will be given to applicants with (1
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. Proficiency in computer programming, including scripting in Python, Fortran, or other computing tools for data processing and modeling. Experience in grant proposal writing within a collaborative environment
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conducting clinical or preclinical proof-of-concept studies Preferred: Experience in physiological signal processing and the application of machine learning to biomedical data Background in computational