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research on the development of new inference methods and algorithms for wide classes of stochastic models. However, research will be conducted in collaboration with biologically oriented researches allowing
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research on the development of new inference methods and algorithms for wide classes of stochastic models. However, research will be conducted in collaboration with biologically oriented researches allowing
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and significant piece of information to the right point of computation (or actuation) at the correct moment in time. To address this challenge, you will focus on developing theoretical and algorithmic
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and algorithmic foundations for goal-oriented, semantics-aware communication strategies that enable efficient, intelligent, and adaptive information exchange in joint communication and control. In
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to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both
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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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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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that yield valid statistical conclusions (inference) on causal effects when using machine learning algorithms and big datasets. The project is part of the research environment Stat4Reg (www.stat4reg.se ), and
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; they make sense to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project
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, signal processing and/or wireless communication. Basic knowledge of and/or experience in working with reinforcement learning/other machine learning algorithms Excellent command of spoken and written