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and have synergiccollaborationeffects. Weexpect a motivatedearlycareer researcher with stronginterest and experience with GIS/earth observation/climateprojection data as well as machine learning models
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP
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Description The Fuqua School of Business at Duke University invites applications for the position of Adjunct Professor to teach the course “Foundations of Capital Markets” in Fuqua’s MMS program in the fall
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YOUNG RESEARCHER IN THE FIELD OF EARLY DETECTION OF THE HEALTH STATUS OF PLANTS USING REMOTE SENSING
and features for the pre-symptomatic detection of changes in the physiological status of plants, developing, training, validating, and comparing predictive machine learning and deep learning models
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atmospheric perturbations, and improving performance under realistic operational conditions. Main activities include: • Designing and developing deep learning models to correct wavefront sensor nonlinearities
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physics-integrated machine learning models—to predict, analyze, engineer, and understand microbial community dynamics. Applications span precision medicine and built environment microbiomes, with a strong
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adapters should be created and tested for previously selected detection methods, which can reliably bypass these detection methods. Be part of change Researching and implementing novel machine learning
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scholarly output. • Lead the design, architecture, and deployment of advanced AI systems, including machine learning, federated modeling frameworks, multi-agent systems, and AI pipelines for dairy farming
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 8 days ago
AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models to uncover how gene expression and mechanical forces interact
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. The postdoctoral fellow will lead efforts to develop novel machine learning models for integrating omics datasets (e.g., genomic, transcriptomic, epigenomic, proteomic, metabolomic) with relevant molecular pathways