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and interdisciplinary data integration develop new AI-based methods, tools, scripts, ontologies and a knowledge graph based on RTG research results and relevant literature provide methodological support
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the ANR. PhD student in Graph Signal Processing for the Characterization of Multipolar Electrograms of Persistent Atrial Fibrillation. Responsible for a significant proportion of brain strokes, atrial
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insights from geometry and topology to discover new applications of machine learning. Multiple positions may be available. Role Requirements The successful candidate must have a PhD (or close to submitting
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graph using RDF, OWL, and related technologies Designing and implementing workflows for data ingestion, integration, and querying across multiple systems Driving use-case studies that demonstrate
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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) Research area: Large Language Models (LLMs), knowledge graphs (KGs), commonsense knowledge Tasks: foundational or applied research in at least one of the following areas: LLMs, KGs, knowledge extraction
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understanding of KB development Working with specific Robotics applications in multiple domains Proficiency with various state-of-the-art Computer Vision models Project 2. - PhD Position in Sustainable AI
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interdisciplinary research and training program. The objective of the open PhD position is to advance current over-the-air-computing (AirComp) approaches for federated and graph-based Embedded AI to account for
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to evaluate. The main research question is how to automatically harmonize the retrieved information allowing a unique analysis and to map them against multiple user-tailored outputs. This is necessary as the
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, specifically modelling the complex interrelations among infrastructure, human operators, and organizational structures using dynamic graphs, system dynamics, Agent Based Models, and discrete event simulations