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, ideally with a background in graph-based or geometric deep learning. Written and oral command of English is essential. Research environment The Max Planck Institute of Biochemistry, Martinsried, was founded
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strong background in image processing and analysis, including deep learning experience with correlative imaging workflows and 2D/3D registration techniques strong programming skills in Python and/or C/C
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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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via the Emmy Noether project “Stability and Solvability in Deep Learning”. This project focuses on mathematically analyzing machine learning algorithms with a particular focus on questions of stability
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close collaboration with a deep-tech startup. You will work in an internationally recognized research environment with access to state-of-the-art cleanroom facilities and collaborate with leading academic
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 1 month ago
and experience Experience with 3D parametric body models (i.e. SMPL/MANO/ATLAS/VAREN/etc.) Experience in Computational Geometry, Computer Vision and Deep Learning Accepted publications in one
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
contexts Strong programming skills and deep learning frameworks Familiarity with generative models (e.g., diffusion, GANs, autoencoders) or virtual staining concepts Excellent teamwork and communication
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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | about 1 month ago
(ML) and deep learning models Pipeline architecture: Building scalable data pipelines and preparing data for model training AI integration: Integrating ML models into existing software architectures
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addition, the candidate should demonstrate research experience in one or more of the following areas: machine learning (e.g. deep learning) for process models, or the optimization of bioprocess development
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about applying deep learning to decode the regulatory grammar of plant genomes and translating predictions into testable biological hypotheses, we invite you to join the Omics Data Analysis and