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estimation of rotating tools. Direct tool wear characterization will be based on optical measurement systems and data processing to achieve wear feature recognition and quantification. Indirect tool wear
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the Western Baltic Sea. The PhD Scholarship includes before-after control-impact (BACI) studies as well as underwater footage to determine fish abundance (MaxN) and biodiversity of mobile fauna (e.g. shore crab
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conversion. The project partner at TUM will focus on the materials production, stability characterization and the catalytic activity tests aspect of the project. The work at DTU will focus on the materials
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on the theoretical foundation of machine learning. Your CV comprises: A strong relevant background within machine learning and mathematics. Extensive experience programming machine learning models. An active interest
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approaches for the computational analysis of time-resolved data on reactions, contribute to teaching and supervising BSc and MSc student projects Qualifications You must have a two-year master's degree (120
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than RGB will be actively researched. Exploring 3D canopy modelling and plant growth dynamics for digital twin integration. Self-supervised learning will generate multi-modal agricultural pre-trained AI
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results. The ability to work collaboratively in a team with an open-minded spirit, embracing both teaching and learning opportunities. A genuine interest in discussing physics and engaging in thoughtful
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limited. We are offering a PhD scholarship for a student to develop ambitious new machine learning strategies for generating AI-ready data. You will work at the frontier of active learning and ML-guided
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assist in preparation and testing of the platforms at the partner institutions collaborate with an interdisciplinary research team in a focused scientific effort contribute as teaching assistant at DTU