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Field
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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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interface graph extraction, large-scale repository mining. Parallel data pipelines. `multiprocessing`, Ray or Dask; checkpointing, heartbeats, cost estimation before launching a run that touches millions
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species and the photochemically formed secondary air pollutants such as ozone and particulate matter. Secondly, a very large number of different hydrocarbons are emitted which makes their inclusion on air
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ecological changes might drive evolutionary processes, how large ecological systems adapt or not to rapid changes, what are the neural mechanisms underlying simple to complex behaviours, how do large animal
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Course location Bonn Description/content BIGS-Land and Food is an integral part of the Faculty of Agricultural, Nutritional and Engineering Sciences at the University of Bonn, serving as a dynamic
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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