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Field
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Recommendation Systems Build LLM-based agent systems for personalized career recommendation that operate over long-term user memory, structured experience representations, and continual information retrieval
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computational models for industrial capacity planning, logistics optimization, material flow analysis, and supply chain analysis. Apply artificial intelligence, machine learning, LLMs, and advanced statistical
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competitors/frameworks (Mojo and JAX) for extreme‑scale heterogeneous systems. The selected researcher will explore how autonomous AI agents and LLM‑driven code generation can co‑evolve with next‑generation
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discipline Strong experience in integrating several of the following components: Deep learning and LLMs for molecular biology Vision foundation models for pathological image analysis Multi-omics datasets (e.g
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or medical imaging. - Technical Skills: Proficiency in programming languages such as LLMs, CNN, DNN, Python, MATLAB, or R, with experience in imaging software and AI frameworks is desired. - Research Acumen
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and experimentation in the area of Multi-agent Agentic AI systems applied to 6G network and service management. By leveraging recent advances in Large Language Models (LLM) and other key agentic tools
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Humanities, Computer Science, or a related discipline with a focus on digital heritage applications. Experience in building RAG pipelines using Google AI Studio Pro, or similar LLM orchestration frameworks
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, LLM, Stata, Python, or R; (f) have track record of publications in high-quality international journals or demonstrated potential for future publications; and (g) have experience in collaborative
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(e.g. via LLMs and RAG), tools for topic modelling, sentiment analysis, opinion mining, trend analysis, and various forms of text annotation need to be integrated in the infrastructure, as well as tools
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the Large Language Models (LLMs). The successful candidate will work on pioneering research projects that push the boundaries of what AI can achieve, particularly in the domain of multimodal learning. You