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, University of Galway), Africa and the Americas. The Chair of Livestock Systems is newly established at TUM includes one postdoc, two PhD students, visiting researchers and technical staff. The team involves a
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is available from 01.11.2025. About us The Chair of Agrimechatronics (Prof. Oksanen) studies especially Intelligent Machines for Agriculture. The research themes include topics like tractors and other
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machine learning technologies. This PhD position is part of the project “Artificial Intelligence for the automated creation of multi-scale digital twins of the built world”, which is funded via the Georg
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, C++, etc.) Knowledge of machine learning, data mining, or related fields Excellent communication skills and ability to work in a collaborative team environment Interest in social science research
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with chemoreception and sensory biological techniques (SSR, GC-EAD, EAG). •Experience in analytical chemistry (GC-FID, GC-MS). •Experience in or willingness to learn statistical data analyses, data
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knowledge in food chemistry and superior interest in food systems biology, food-related research • Keen interest in learning and applying experimental biophysical techniques, in particular AFM (essential
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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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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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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
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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