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neuroimaging methods to better detect disrupted function following neonatal brain injury, and identify new more energy efficient learning algorithms that could reduce the economic and environmental cost of AI
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neuroimaging methods to better detect disrupted function following neonatal brain injury, and identify new more energy efficient learning algorithms that could reduce the economic and environmental cost of AI
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neuroimaging methods to better detect disrupted function following neonatal brain injury, and identify new more energy efficient learning algorithms that could reduce the economic and environmental cost of AI
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: Reviewing existing quantum algorithms relevant to fluid dynamics and atmospheric modeling. Evaluating their compatibility with the structure and requirements of current NWP models. Identifying potential
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imaging algorithm in Hypoxic Ischaemic Encephalopathy). The Sophie project aims to develop a Neonatal MRI Biomarker for newborns with Hypoxic Ischaemic encephalopathy that is predictive of altered brain
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-time power amplifier linearization using Field Programmable Gate Array (FPGA) platforms. The research will focus on the development of intelligent Digital Predistortion (DPD) algorithms capable
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-enhanced localization algorithms, intelligent path planning techniques, and adaptive navigation strategies capable of operating in environments with obstacles, moving personnel, and changing factory layouts
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(IIoT) sensors, real-time data analytics, machine learning algorithms, and Digital Twin technologies to monitor equipment health, predict failures before they occur, and recommend optimal maintenance
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the effects of new gene therapy-based treatments for acquired and genetic epilepsies. The main role is to perform in vivo studies testing minimally-invasive methods to deliver gene therapies in preclinical
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and apply a wide range of techniques, including cultivation of microorganisms, the isolating and cultivation of immune cells, and the construction of genetically modified microorganisms. Molecular, cell