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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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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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, 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
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). Excellent communication skills in English are essential. Desirable requirements: The following will be considered strong assets: practical research experience with AI, Large Language Models (LLMs), and
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the German Research Foundation (DFG). The priority area contributes to a better understanding of language technology (in particular, large language models, LLMs) and its applicability in the sciences, with a
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | about 11 hours ago
rulings, shifting judges’ roles from writing to supervising. We aim at developing a benchmark to test whether LLMs can produce legal reasoning comparable to trained lawyers. (PIs: Engel, Gummadi) In
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censorship. Core responsibilities include: Develop LLM-driven knowledge graphs that construct probabilistic historical priors from bibliographic records, trial transcripts, censorship lists, and apprenticeship