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wireless communications systems. For details, you may refer to the following: https://wwwen.uni.lu/snt/research/sigcom We’re looking for people driven by excellence, excited about innovation, and looking
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of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD project falls under the collaboration between
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, including deep reinforcement learning, large language models, and the theory of deep learning. The candidate will develop DRL algorithms for online and off-line tasks, for robotic applications and possibly
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(Task T2.4). Implement algorithms for training with limited data (Task T3.1). Develop prototypes for use cases in Smart Cities (Task T4.3). These tasks are part of the IDEALCV-CM project, reference: TEC
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engagement—particularly within ESG disclosures—its implementation is currently challenged by ethical concerns regarding data privacy, algorithmic bias, and cultural resistance. This project aims to contribute
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. You can continue your career journey with us! The Slomka Laboratory focuses on developing innovative methods for fully automated analysis of nuclear cardiology data using novel algorithms and machine
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details and cross-layer interactions with communication and hardware can further affect the actions available to the adversary. We seek to develop analysis and design algorithms that incorporate cross-layer
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radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical imaging. Within our research
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developing prototype systems and algorithms • Experience with MRI and/or fMRI data (human data preferred) • Experience with high resolution and high field MRI and/or fMRI • Excellent verbal and written
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interface, and all the way to quantum algorithms and applications. The long-term mission of the programme is to develop fault-tolerant quantum computing hardware and quantum algorithms that solve life-science