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to investigate the feasibility and advantages of new design principles for transceiver frontend design, including data converter solutions. Expected outcome is a disruptive and novel approach to co-optimized radio
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material layers that can be optimized to specific battery chemistries and flow phenomena from the microscale up. The developed technologies will be validated in half-cells and full working batteries
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affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow phenomena from
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including modelling to determine the localization, distribution, and potential on/off-target effects of ONs in vivo. The final aim is to gain know-how on how to optimize formulations for different
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principles for transceiver frontend design, including data converter solutions. Expected outcome is a disruptive and novel approach to co-optimized radio transceiver design with measured and verified state
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decision-support tools for energy-aware planning, predictive maintenance, and resource optimization, -use robotics, autonomous systems, IEC 61499, and digital twins to design and evaluate distributed control
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to ensure optimal tissue concentrations during surgery. The PhD student will utilise national and international arthroplasty registry data, adapt in vitro diagnostic tools such as the Minimum Biofilm
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division of the Department of Electrical Engineering . You will be supervised by senior researchers with expertise in robotics, machine learning, automatic control, and optimization. The group leads and
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learning, mathematical statistics, optimization, and robotics. Experience from programming in C/C++ or Python is also meritorious. Willingness to work in an inter-cultural, international, and diverse group
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learning, mathematical statistics, optimization, and robotics. Experience from programming in C/C++ or Python is also meritorious. Willingness to work in an inter-cultural, international, and diverse group