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Scientific Assistant (f/m/d) Junior Helmholtz.AI Consultant / Completed university studies (Bache...
) in Computer Sciences, in the field of Natural Sciences, or related fields # Experience and knowledge # in supervised learning computer vision architectures (Unet, StarDist, Cellpose, CPN, nnUnet, SAM
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Max Planck Institute of Molecular Cell Biology and Genetics, Dresden | Dresden, Sachsen | Germany | 23 days ago
and organoid systems that capture key aspects of tissue architecture, cellular heterogeneity, epithelial-stromal interactions and disease-associated states (Dowbaj et al., Nature 2025; Yuan et al
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different model structures, such as graph neural networks, graph transformers, multilayer perceptrons, or other suitable architecture Evaluate model performance with respect to prediction accuracy, physical
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Earth system modeling framework remains technologically advanced and capable of efficiently exploiting emerging HPC architectures. Emphasis will be placed on heterogeneous computing and the optimal use
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architecture to code generation, testing, deployment, and maintenance Participation in a major national research project involving leading academic and industrial partners The opportunity to contribute your own
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at the intersection of structural engineering, architecture, robotics, and computation? As part of an EU-funded research project, we are establishing an interdisciplinary research team, in partnership with leading
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deep learning Familiarity with common neural network architectures, including Transformers, 1D Convolutional Neural Networks (1D CNNs), and related models Solid understanding of fundamental machine
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to help define the architecture, interfaces and future direction of our software rather than simply contribute to a predefined codebase. We are particularly interested in exploring how AI-assisted and
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claims that the choice of loss function and architecture only has a limited impact on performance [7]. This blanket statement is to be critically and thoroughly examined in this work to clarify when
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verification (AV) is to classify whether two or more texts were written by the same author (Y) or not (N). As in most AI fields today, the most powerful models are usually based on transformer architectures