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. Details about each PhD project and Principle Investigator can be found on our website: https://dresdencondensates.org/projects/ . Tasks: Performing independent research work on one of the scientific themes
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fields. What we offer State of the art on-site high performance/GPU compute facilities A team of 30+ expert colleagues A family-friendly, green campus with on-site kindergarten Ample training
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Programme? Not funded by a EU programme Reference Number JP/007/25 Is the Job related to staff position within a Research Infrastructure? No Offer Description The Faculty of Protestant Theology invites
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Computer Science, Bioinformatics, Biomathematics or a similar subject High level of intrinsic motivation and enthusiasm for translational research connecting academia and industry Clear understanding of statistical
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focuses on the development and operation of novel ultrafast high-power laser systems covering the spectral range from ultraviolet to mid-infrared as next generation pump-probe laser systems at the FLASH XUV
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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 3 days ago
submitting the application, contact Susanna Pohl . Included benefits Successful applicants will have support for travel, publications, and high-performance computing through the Max Planck Computing and Data
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., erythrocytes, leukocytes, platelets) in blood samples using fluorescence-assisted RT-DC measurements will be established. By developing and applying defined staining protocols, you will generate high-quality
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foundation and practical experience in machine learning and reinforcement learning Experience in training and evaluation of machine learning models on large high performance computing infrastructure Existing
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Germany Application Deadline 31 Oct 2025 - 22:59 (UTC) Type of Contract To be defined Job Status Other Offer Starting Date 10 Sep 2025 Is the job funded through the EU Research Framework Programme? Not
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, static user representations, and data sparsity. While deep learning models offer improvements, they often come with high computational costs and require frequent retraining, which limits their scalability