25 parallel-and-distributed-computing PhD positions at Linköping University in Sweden
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application! We are now looking for a PhD student in Computer Vision and Learning Systems at the Department of Electrical Engineering (ISY). Your work assignments Your task will be to analyse and adapt vision
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application! We invite applications for a fully funded PhD student position to join the research group of Andrew Winters to work on challenging problems in Computational Mathematics for accurate and reliable
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distributed computational pipelines and optimizing communication costs. You will also contribute to the integration and testing of the models in real D-MIMO environments, in close collaboration with a PhD
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dynamics. Particular emphasis is placed on opinion dynamics as well as distributed problems in coordination, optimization, and learning. The research encompasses both theoretical and computational aspects
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formation and how local dose is distributed. In the longer perspective, this knowledge will support optimization and translation of bioelectronic implants towards clinical application. In this project, you
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identify, analyze, and evaluate strategies that can make the last-mile distribution more sustainable than today. You will, for example, analyze different scenarios with mixed vehicle fleets, charging
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application! Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular
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graduated at Master’s level in machine learning, statistics, computer science, fluid mechanics, or a related area that is considered relevant for the research topic of the project, or have completed courses
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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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, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on methods that reduce compute, energy