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application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through scaling model sizes, training budgets
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principled new models and methods, for modern machine learning problems. Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models
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scaling model sizes, training budgets, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on
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an increased interest in adapting and developing the latest machine learning methods for the purpose of malware detection, and preliminary results are encouraging. The specific goals of this project include
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Computing Science at Umeå University is looking for a doctoral student in machine learning
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machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome, etc.) development of predictive models and digital decision-support
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, where AI models are trained without having all data in a single computer. This makes it possible to use larger datasets for training, without sending sensitive data between hospitals. The goal is to
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for Communication Systems carries out research, undergraduate and postgraduate education in communications engineering, statistical signal processing, network science, and decentralized machine learning. Welcome
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harmful content or incorrect code. This project introduces a new idea: instead of relying on opaque, heavy machine-learning models, it learns transparent, rule-based rewriting systems that can transform
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi