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Field
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peer-reviewed journals and/or top-tier conferences. Knowledge & Skills: Strong foundation in machine learning, deep learning, and algorithm development. Proficiency in scientific programming (e.g
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electrical stresses. Specific goals include: - Development of a hybrid model combining degradation indicators and AI-based algorithms. - Integration of the model into an online monitoring framework
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Center for Drug Evaluation and Research (CDER) | Jefferson, Arkansas | United States | about 23 hours ago
workflows; GenAI based algorithm and agent development for causality assessment; Web-based tool development for the support of dataset collection; Performance evaluation and validation for the developed AI
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manage research datasets, including development of analytic workflows, REDCap data collection tools, algorithm development, and validation of NLP pipelines · lead the development of scholarly outputs
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Responsibilities: Design and implement advanced AI/ML models for healthcare applications, including predictive analytics and generative AI solutions. Develop and validate digital phenotyping algorithms using
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developed countries, smartphone penetration exceeds 80%. The automatic transport mode detection (TMD), when effectively exploited, possibly using some kind of machine learning algorithm, provides more
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designing and improving general algorithms for algebraic cryptanalysis, or developing concrete attacks against proposed ciphers. This PhD position is an opportunity to explore and shape your own research
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 23 hours ago
Medical Dictionary for Regulatory Activities (MedDRA)-based search algorithms compared to a novel algorithm that leverages other features (e.g., patient age) for identification of cases of prenatal drug
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analysis; (b) develop and implement algorithms for 3D perception (e.g. segmentation, localization and mapping); (c) design and execute experiments to evaluate, validate and refine algorithms and
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international collaborators across clinical, academic, and industry settings to develop privacy-preserving machine learning approaches, federated learning frameworks, and interpretable algorithms for multimodal