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Field
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viability data to discover new biomarkers and treatment strategies. You will work in a highly interdisciplinary environment spanning oncology, cell biology, imaging, bioinformatics and machine learning, with
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: Research activities will be conducted in BSL-2+ laboratories. Learning Objectives: Under the guidance of a mentor, the participant will gain experience in: Animal studies: mouse and large animal inoculation
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, New York 14850, United States of America [map ] Subject Areas: Data Science / Statistics , Applied Mathematics , Artificial Intelligence , Bayesian Statistics , Big Data , Scientific Machine Learning
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Academic Level B: Completion of a PhD in the field of Computer Science/Artificial Intelligence. Software engineering expertise, including design and implementation of AI-based models (machine learning, deep
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of these publications and your part in these. Selection Criteria: Essential PhD in Computer Science, Informatics, Data Science, Statistics, or a closely related area. Substantial research experience in applied Artificial
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geometry, and/or data science. Specific topics of focus include, but are not limited to, linear response, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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than 9,000 digitized patient journals with ongoing data collection and possibility of further expansion. The candidate will work at the intersection of AI, information retrieval, multimodal learning, and
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of geoscience? Improve deep learning methods for multi-modal image data applied to industrial challenges in the energy sector? Advance the use of seismic tiles for extracting information about geological layers
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policy relevance. Coordinate modelling activities across multiple projects and deliver high-quality outputs on time. Integrate new methodologies, including AI and machine-learning approaches
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, Natural Language Processing, and structured knowledge representations. As a researcher within Integreat, you will contribute to developing next-generation Machine Learning for advanced data analysis