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expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security
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education in digitalization and electrification including renewable energy sources, electric vehicles, industrial IoT, AI, 6G communication and wireless sensor networks as well as research and education
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algorithms. Our research integrates expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems
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. At the division, we conduct research within functional porous materials for energy and environmental applications, nano biomaterials, sustainable electrical energy storage and energy transformations
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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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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for the rapid conversion of biomass into hard carbons, significantly reducing the time and energy required compared to traditional processes. Additionally, the collaborative research aims to identify optimal
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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
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the research area Medical Science, specifically neuroscience with the title: ”Cell Signalling at the Blood Brain Barrier”. The research group conducts translational research with a focus on
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methods that reduce compute, energy usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and