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Field
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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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project. This position will require an in depth knowledge of a specialized field, process, or discipline and may involve organizing and implementing complex research plans, the development of methods
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progress. Ability and willingness to work some flexible hours. Extensive experience in large-scale pre-training of large language model. Experienced in developing machine learning algorithms and large
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The Centre for Urban Solutions is to provide leadership in developing innovative solutions and sustainable technologies for space creation and urban infrastructure development. The School of Civil
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 22 days ago
/lanfear-lab-mutation-molecular-evolution-and-phylogenetics The Person You will need to have a detailed understanding of cutting-edge methods and algorithms in maximum-likelihood phylogenetic inference, as
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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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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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estimations in humanitarian and public health contexts by developing reproducible, multilingual workflows for social media analysis, building data pipelines in R/Python, and creating open-source tools for text
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, particularly Python; (ii) use of parameter optimization algorithms, particularly PEST and PEST++; (iii) remote sensing applied to the water cycle; and (iv) application of machine learning techniques to spatio