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Field
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. Contributing to the development of LLM-based systems, including prompting strategies, tool use, evaluation, retrieval-augmented workflows, and adaptation. Designing and running user studies to evaluate how
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-end of the system utilizing full technological stacks. The ideal candidate must have excellent programming skills and experience with setting up open-source LLM environments is a clear advantage. Given
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selection criteria Experience with agentic AI systems, LLM-based agents, tool use, planning, or multi-agent systems. Knowledge of trustworthy AI topics such as interpretability, explainability, uncertainty
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graduate degree (LLM, JD, MS, MA or equivalent); have demonstrable academic or professional experience in competition/antitrust law, policy, or enforcement; have demonstrable academic or professional
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. This proposal should not be considered as entirely prescriptive—you are encouraged to make this project your own in consultation with your supervisors. Your qualities You: have obtained a graduate degree (LLM, JD
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development, model hosting, deep learning, LLM fine-tuning experience (e.g., with huggingface transformers and parameter-efficient methods such as LoRA/QLoRA), model evaluation experience. (Please briefly
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things, intelligent assistants and company-specific search functions. However, we notice that companies still have many questions about reliability: modern LLMs still do not guarantee correctness and
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the use of (multi‑modal) Large Language Models (LLMs) and other AI techniques to support the automated generation and adaptation of DWIs from DPP data. This research will explicitly assess which types
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
or experience in LLMs, tool-using agents, or agentic workflows. Experience in optoacoustics, ultrasound, optical imaging, spectroscopy, or related technologies. Interest in entrepreneurship, innovation transfer
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platform that supports multimodal data ingestion, knowledge retrieval, and LLM-based workflows for the delivery of evidence-based coaching strategies, including motivational interviewing. This includes