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. Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to
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on "Development of AI Models for Capturing Connections in System Diagrams" is the development of deep learning models to capture connections in scanned diagrams. By the end of the work, a method should be
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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), and computational modeling (deep neural networks). We apply multivariate analysis methods (machine learning, representational similarity analysis) and encoding models. Job description: This is an open
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supported by an external team of deep-learning experts. You will also become an integral part of the Multiscale Cloud Physics Group currently being established by Dr Franziska Glassmeier at the Max Planck
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generating a high-quality training dataset to support the development of the AI foundation model Contributing to the design and implementation of advanced deep learning architectures (e.g., Transformers, CNNs
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, geometallurgy or related field Experience in either stochastics, deep learning or minerals processing is needed Structured and solution-oriented working style, analytical thinking and above-average committment
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: machine learning, data analysis, energy technology Experience with common deep learning and data analysis frameworks (e.g., PyTorch, Numpy, Pandas, sklearn, etc.) Independent, structured, and reliable way
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multimodal datasets Design and fine-tune machine learning and deep learning models to extract meaningful patterns and predict metastatic behavior Collaborate closely with experimentalists for mechanistic
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we