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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
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in analytical theory from Nancy and the long-standing experience in sophisticated computer simulation studies from Leipzig, promising unique prospects in advanced education of PhD students via research
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-regulatory networks. Following cis-GRN network reconstruction and formal graph analysis, we will identify key regulatory factors governing cell-type specific response to CMT-causing mutations. Finally, we will
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and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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. The integration of Knowledge Graphs (KGs) with AI agents will link the data and the actions taken by AI agents. Reinforcement Learning from Human Feedback (RLHF) will enable AI to learn and adapt based on real-time
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learning, distributed systems, and theory of networks. Within these three areas, we are currently working on several projects: graph neural networks, natural language processing, algorithmic learning, fault
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and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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integrate these data into the cis-regulatory networks. Following cis-GRN network reconstruction and formal graph analysis, we will identified key regulatory factors governing cell-type specific reponse to CMT
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Job related to staff position within a Research Infrastructure? No Offer Description Interested in doing research on the boundary of probability theory and geometry? We are seeking a skilled PhD
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into the data and models used to generate outcomes. The integration of Knowledge Graphs (KGs) with AI agents will link the data and the actions taken by AI agents. Reinforcement Learning from Human Feedback (RLHF