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Field
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into district heating networks (DHN), and to study the architecture, sizing, and control strategies for its optimal integration into such networks. Currently, sensible heat storage remains the most widely used
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: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design Creating and validating digital twin architectures that incorporate physical laws and constraints
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emerging spectrum (FR3, MMW, sub-THz/THz) Extreme massive MIMO communications Analog, digital and hybrid beamforming architectures Reconfigurable intelligent surfaces Machine learning for wireless
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, shipping and architecture. With scientific excellence as a basis, Chalmers promotes knowledge and technical solutions for a sustainable world. Through global commitment and entrepreneurship, we foster
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prior to the application deadline Research experience with deep learning architectures (e.g. Transformers, diffusion models, graph neural networks) applied to multimodal data. Proven expertise in time
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for new physics and performance studies. Candidates with experience in modern AI/ML methods—such as transformer architectures, tokenization strategies, and embeddings—are especially encouraged to apply
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combine advanced remote sensing and 3D data with ecological theory to deepen the understanding of the role of tree architecture in forest ecosystem vitality. About the position Global warming has triggered
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execution time (WCET). The postdoc position focuses on compiler support for WCET analysis for time-predictable architectures such as Patmos/T-CREST. Furthermore, it is expected to join the development of a
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, which brings together 4 partners: LTM/CNRS, pheliqs/CEA, Institut Néel/CNRS and CREAH/CNRS. The aim of the project is to manufacture betavoltaic sources using a new core-shell wire architecture
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/B/ST5/02500, Controlling Reaction-Diffusion Mechanisms for Advanced Self-Assembled Architectures: Periodic Precipitation and Chemical Gardens Leader Dr hab. Jan Paczesny The project explores