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Field
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group, a multinational insurance company. Tasks Your duties will include: Literature research Designing, implementing, and evaluating novel machine learning approaches to detect building attributes from
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informal learning settings with a specific focus on museums. The lab uses a variety of methods (e.g. eye-tracking, VR) covering the entire continuum from computer-based tasks to real-life engagement. With
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quality control tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep
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quality control tools for distributed models, and iii) robustness to data and model poisoning attacks. In this context, we are looking for a PhD Candidate who has a strong background in machine/deep
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for our clients from numerous industries. Overarching top topics at Fraunhofer ITWM are Machine Learning as well as Artificial Intelligence and Renewable Energies or Sustainability. In addition, Next
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, Computational Linguistics, Data Science or a similar field Good theoretical knowledge and practical experience with Natural Language Processing (rule-based and/or machine learning) Software Engineering Motivation
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evaluating machine-learning models. Expertise in in the field of Building Information Modelling and geometric modelling is greatly beneficial. Excellent English and the willingness to learn the German language
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most data sources are in German. Candidates should work independently and be willing to familiarize themselves with new topics and methods, including machine learning and the handling of large data sets
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for this position, the candidate should possess in-depth skills in programming and hands-on training and evaluating machine-learning models. Expertise in in the field of Building Information Modelling and geometric