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that are relevant to industry demands while working on research projects in SIT. About the DIGNIFIED programme The DIGNIFIED programme is dedicated to developing elderly-friendly, textured modified foods with
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. Responsibilities include conceptualizing and implementing statistical and structural models, developing scalable algorithms for system optimization and control, conducting policy-relevant economic analysis
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 27 days ago
expected to develop and run workshops to introduce your work to end users. For further detail on the Lanfear Lab, our ideals and goals please visit our webpage: http://biology.anu.edu.au/research/labs
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The position An exciting postdoctoral position in method development for spatio-temporal medical data is available in the UiT Machine Learning Group at the Department of Physics and Technology . Goal: Develop
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Center for Biologics Evaluation and Research (CBER) | Silver Spring, Maryland | United States | 3 days ago
learning algorithms, as well as the adaptation and optimization of existing tools. This research aligns with CBER’s efforts to enhance the development, operations, and management of FDA’s High-performance
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aims to strengthen interdisciplinary research among faculty, universities, research centers, industry partners, and government agencies to address global quantum challenges and prepare a new generation
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progress in machine learning and artificial intelligence, the successful candidate will have primary responsibility to develop, implement, and test multimodal machine learning algorithms to analyze and
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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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on application logic rather than underlying algorithm development), and the capability of independently driving the full "Data-AI-Deployment" process; (c) the ability to evaluate the fit between AI applications
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on the assurance, risk assessment, and insurability of AI systems, supporting the development of trustworthy, reliable, and accountable AI technologies across critical sectors such as finance, insurance, and digital