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and machine learning to establish a modeling framework that uses omic data for providing effective degradation rates of biomolecules and predictions of their impact on soil organic matter turnover
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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Research Assistant (m/f/d) in the field of Theoretical Ecology and Evolution or Computational Biolog
, there is the opportunity to work on your own research topics and establish a junior research group. Your duties: Support ongoing research projects by developing computer models and performing computer-based
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are developing AI that can map welding processes in the automotive industry based on simulation data. We are looking for a student assistant with an interest in machine learning and finite element simulation. Your
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or CNC machines You have already worked with Python or other data analysis software, or are motivated to acquire the relevant skills What you can expect 👥 Team spirit: Creative and interdisciplinary
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. The Leibniz-LSB@TUM comprises a unique and world-leading research profile at the interface of Food Chemistry and Biology, Chemosensors and Technology, and Bioinformatics and Machine Learning. Leibniz-LSB@TUM’s
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between the fields of computer sciences and architecture. The focus lies mainly on Building Information Modelling, decision-support methods in urban planning and knowledge-based design methods. As part of
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the INW-1 machine learning team on data handling, online analysis, design of experiments (DoE), and data categorization to enable efficient and automated evaluation of operando experiments Collaboration
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novel machine learning-guided approaches. The position is located at TUM Campus Heilbronn. Your qualifications Strong background in computer science, AI, or related areas or similar fields. Solid
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electrochemical impedance spectroscopy (EIS) directly during the disassembly process to classify the cells for their reusability. A pre-trained machine learning model for assessing cell condition based on EIS data