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of internationally visible, foundational research in AI-driven semantic structure extraction, automated reasoning-flow modeling, and adaptive content generation. The research focuses on methods for analyzing and
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and implementing NLP pipelines for clinical text processing, semantic annotation, and representation learning Developing embedding-based representations of clinical variables and documents to support
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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation
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support: Assist with prototyping and evaluation of multi-agent workflows (e.g., information retrieval, semantic search, recommendation logic, human-in-the-loop processes). Programming and implementation
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Internet, Earth Observation, and Autonomous Transportation. As far as technical enablers are concerned, we leverage expertise on advanced technologies including semantic/task-oriented data processing, signal
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the development of a higher-layer semantic planning, the decomposition of the plan in subsegments and dedicated controller (such as MPC) for combined planning and control on a subsegment of the XS-Graph
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semantics e.g. for Earth Observation and Internet of Things, Telecom GPT, Open RAN/AI-RAN integration Infrastructure: MFSS - Multi-functional structures. ISAC - Integrated sensing and communications
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mining. In-depth knowledge of the design, analysis and implementation of algorithms for large text corpora, including efficient data pipelines and clean experimental design. Strong NLP skills for semantic
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the brain without requiring deep electrodes. BRAINET sets out to develop skull implantable stimulation hubs forming a communication network for 3D targeting by self-organization, providing semantic