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semantic representation models for sign language. Such representations are key to allowing SL to be efficiently processed by large language models (LLMs), and will lead to machine learning models that can
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experiments, and machine learning (ML) to understand and predict multiscale transport phenomena in fuel cell systems. In particular, the postdoc will bridge pore-scale simulations and macroscale performance
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description and duties The postdoc fellow will conduct research at the borderline between the fields of information visualization / visual analytics as well as machine learning in close collaboration with
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communication theory, machine learning, complex networks, and optimization. The employment This employment is a temporary contract of two years with the possibility of extension up to a total maximum of three
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analysis, statistical modelling, linear mixed models, and machine learning among others. The position is well suited for an individual interested in quantitative genetics and data analysis that wishes
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modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an innovative startup partner. You will be
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experience with machine learning techniques in general and neural networks in particular will be highly beneficial. About the employment The employment is a temporary position of 2 years according to central
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be made for special reasons). Experience in research on digital cultures. Proficiency in AI methods (e.g., NLP techniques, machine learning, and large language models, LLMs)—including web scraping
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of information visualization, visual analytics, applied machine learning but possibly also in the areas of the domain experts. Within the DISA environment, large and complex data sets from various domain areas
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information and communication theory, machine learning, and signal processing. We offer a dynamic, supportive, and international research environment with around 150 employees from more than 20 countries. Our