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Job description:Title: DC13, PhD fellowship in explainable machine learning techniques to support the design of plant-based fermented food products – Development of a serious game to support the
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in Computer Science or a related field Experience in software development Excellent programming skills Familiar with basics of machine learning Interest in mobility technology and/or simulations, with
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focus on biometrics, with special emphasis on computer vision and machine learning. As one of our research associates, you will also be part of the National Research Center for Applied Cybersecurity
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frameworkslike PyTorch2) Practical experience in advanced computer vision and deep learning applications3) Knowledge of MATLAB, C/C++ and fluorescence imaging is beneficial but not mandatoryMinimum Qualification
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machine learning potentials for supramolecular metallacages and enzymes. Efficient and accurate potentials will be developed and applied to study confinement effects in metal-containing systems, such as
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the complex food composition, which can lead to masking effects and molecular interactions in the food matrix. The applicant will work on the development of machine learning prediction models
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19.04.2024, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Computer Engineering at TUM has open positions for a doctoral researcher (TV-L E13 100%, 3-4 years
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on knowledge representation and machine learning at scale. It is particularly interested in models with rich semantics, e.g., RDF knowledge graphs. You will be supervised by Prof. Dr. Axel Ngonga (head of group
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that generalize well on different domains and types of radar sensors. Develop and improve deep learning algorithms for radar! Original research, collaboration with machine learning and radars experts from academia
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Description The research team at the Bosch Center for Artificial Intelligence (BCAI) provides the opportunity for candidates currently enrolled as PhD students in machine learning, computer vision, or a related