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Field
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recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs
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of funding. FLSA Exempt position, not eligible for the overtime provisions of the FLSA. Minimum salary threshold must be met to maintain FLSA exemption. Visit The Office of Postdoctoral Affairs to learn more
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standardize large-scale multi-omics datasets and build databases Perform integrative and exploratory analyses of multi-omics datasets and apply machine learning methods to uncover underlying biological
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day 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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upon future funding. Qualifications Required Education and Experience Appropriate PhD in a related field. Preferred Qualifications Experience with machine learning and deep neural network techniques
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Investigator, the Postdoctoral Associate will conduct specialized cardiovascular research techniques and procedures with focus on artificial intelligence and machine learning platforms that will promote
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scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
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project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a collaboration between multiple research