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, algorithms, and programming. Knowledge and experience in artificial intelligence and machine learning is expected, but not required. Knowledge and experience in deep learning and generative AI is considered
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environment. Embodied AI requires tools, algorithms, and techniques to cope with real-world challenges including but not limited to uncertainty, physical constraints, scarce data, and high variability. In
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research subject for this position is development of distributed processing strategies and algorithms for Large Intelligent Surfaces, including both joint baseband processing and synchronization across
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, and numerical ship analysis. The goal is to develop a digital twin—a virtual replica of a ship’s physical systems—that combines real-world sensor data with advanced numerical models of hull, engine, and
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performance through the integration of AI, big data analysis, and numerical ship analysis. The goal is to develop a digital twin—a virtual replica of a ship’s physical systems—that combines real-world sensor
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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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/or cultivation. PhD in Environmental Engineering and/or in sustainability issues related to cultivation. In-depth understanding of how advanced technological solutions (e.g., sensors, agrivoltaic
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the European Regional Development Fund. Subject description The subject includes signal processing with emphasis on development and optimization of algorithms for processing single and multi-dimensional signals
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Fund. Subject description The subject includes signal processing with emphasis on development and optimization of algorithms for processing single and multi-dimensional signals that are closely related
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district heating networks, within the framework of the project "Data Analysis for Peak Load Stabilisation in District Heating Networks (DAS)". The work includes: design and implementation of RL algorithms