•Experience with:Current methods, tools, languages and frameworks in computer vision and deep learning (e.g., Linux, Bash, Python, Pytorch, Gradio, Numpy, Pandas, Huggingface, Ollama) -LLMs and foundation models in general - In interdisciplinary cooperation with the local Herbarium Haussknecht and other partners all over Senckenberg, we create cutting-edge approaches for publication in high-ranked computer vision, machine learning and interdisciplinary venues with the ambition to convert the novel methods into directly applicable tools usable in collection-based research at Senckenberg and beyond. •Establishment of own projects, research questions and approaches for image and data analysis of collection data with focus on broadly applicable methods able to deal with little available data (e.g., zero-shot, few-shot or weakly supervised approaches and foundation models)
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