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11.11.2024, Academic staff In the project “BIG-ROHU” (BIG Data - Rotor Health and Usage Monitoring), a system is being developed which provides information on both the health and the actual stress
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,orarelatedfield · Strongbackgroundinquantitativemethods,statistics,anddatascience,with demonstrated experience in empirical research and data analysis of large datasets
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Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps
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office and 30 days of paid holidays • An amazing team and the possibility of getting involved in something big • Publications in top-tier journals and conferences • Working on and with an actual rocket
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-based AI for multidisciplinary tumor boards. The successful candidate will develop LLM-based pipelines, semantic harmonization, and transformer-based temporal models on a large multimodal oncology dataset
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architectures for foundation models The work will combine methodological development with large-scale experiments, aiming for contributions at leading machine learning and computer vision venues such as NeurIPS
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) and handling big data (sequencing, mass spectrometry, or similar) • Enthusiasm and background in establishing techniques in molecular biology, data management (big data, sequencing analyses) and related
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for Explainable Precision Medicine, a consortium of 18 partners from 12 European countries. Full-time, TV-L E13, fixed-term for 48 months. Why this position is unique This PhD position offers large-scale multimodal
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will be at the TUM Campus Garching. Description Do you thrive on challenging research projects that bridge the gap between theoretical data science and large-scale practical application? We have an
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to generating synthetic medical data with formal privacy guarantees. The work involves both theoretical analysis and large-scale experimentation on medical imaging data. Results will be published at leading