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to develop deep learning models for analyzing whole-slide histopathology images, as well as natural language processing (NLP) methods for clinical records such as pathology reports and electronic health data
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availability of large-scale genotype data across Europe, along with detailed register and survey data from several countries, to map out how specific genetic factors covary with and interact with our
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 19 days ago
computing infrastructure for OGD. DQMM is also developing innovative quantitative approaches to improve regulatory decision making for generic drugs by fully utilizing the large amount of data available
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H100s). Ideal candidates will have a strong interest and proven experience in designing, understanding, or engineering large AI models or their applications. Current PLI projects involve work with
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
experience is increasingly one of large-scale systems of people, whose actions and interactions are influenced by our digital, physical and biological environment. The ANU College of Systems & Society (CSS
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cooperative education (co-op) program in the country with students earning $94 million annually through paid experiences, an academic health system, and as a member of the Big 12 Conference. The university
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such as: Causal inference and the design and analysis of experiments Reinforcement learning and sequential decision-making Analysis of complex systems, networks, and large-scale data Machine learning
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. Preferred Qualifications Experience analyzing large clinical document corpora and linked structured data; experience with oncology datasets or navigation pathways is an asset. Experience evaluating patient
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and Data Sharing Agreements across urban, rural, and remote EDs, enabling large-scale analyses and new surveillance initiatives. Building on this foundation, the network is now piloting influenza
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big data and advanced methods for modeling exposures, we will establish real-world patterns of hormonal contraceptive use and mental disorders, and identify potential moderating factors such as