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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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position in machine learning (ML). This position offers a unique opportunity to conduct both basic and applied research in concert with collaborators working on diverse scientific and security problems
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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surveillance and preparedness planning using multiple modeling approaches. The successful candidate will develop and implement statistical and machine-learning models, integrate multi-source ecological datasets
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] Position Title: Position Type: Postdoctoral Position Location: Lemont, Illinois 60439, United States of America [map ] Subject Area: High Energy Physics / Machine Learning Appl Deadline: none (posted 2025
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strong publication record (first-author papers in high-impact journals preferred). Demonstrated expertise in at least two of the following areas: AI/machine learning for biological modeling (e.g., virtual
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, integrate device engineering with clinical workflows, and apply artificial intelligence and machine learning for automated image and signal analysis, tissue classification, and real-time diagnostics
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-driven machine learning approaches, they will improve our understanding of nutrient flows in agricultural landscapes. The postdoc will contribute to the development of databases representing the current
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-driven machine learning approaches, they will improve our understanding of nutrient flows in agricultural landscapes. The postdoc will contribute to the development of databases representing the current
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disciplines. Familiarity with contemporary AI systems (e.g., machine learning, generative models) at a conceptual or applied level. Experience with qualitative or mixed research methods (e.g., ethnography