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Research Associate (Postdoc) in Animal Network Ecology § 28 Subsection 2 HmbHG Institution: Faculty of Mathematics, Informatics and Natural Sciences, Department of Biology, Lab of Animal Network
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samples, pregnancy cohorts, and cutting-edge AI-driven analyses Decoding the molecular mechanisms of pregnancy-mediated immune suppression using high-dimensional multi-omics data Validation of computational
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the Research Training Group "AirMetro - Technological & Operational Integration of Highly Automated Air Transport in Urban Areas" (RTG 2947) , funded by the German Research Foundation (DFG
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position, you will have the appropriate qualifications. Please send us a meaningful CV and references. motivation, initiative, flexibility, and willingness to perform a strong independence and reliability as
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Max Planck Institute of Immunobiology and Epigenetics, Freiburg | Freiburg im Breisgau, Baden W rttemberg | Germany | 2 months ago
on transposable elements and/or computational analysis of repetitive sequences Experience in experimental embryology Experience with microscopy and image analysis Experience with genomics datasets We offer Located
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training schemes for large models on high-performance computers like JUWELS and JUPITER at the Jülich Supercomputing Center. If you`re passionate about AI research and eager to make a real-world impact, we
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. Classical cryptographic techniques face inherent limitations, especially regarding future threats from quantum computers or AI-driven adversarial strategies. Physical layer security offers a promising
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Max Planck Institute for Gravitational Physics (Hanover), Hannover | Hannover, Niedersachsen | Germany | 3 months ago
scientific endeavors. Your Duties and Responsibilities Execute functional and performance tests of the equipment under test according to ECSS standards. Set up, operate, and validate test benches, measurement
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include: Performing numerical simulations of magnetic systems to generate magnetometry data for the ML training process, Implementing and testing various neural network architectures to optimally analyze
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, reliability, and consistent behavior. Learning-based controllers can achieve high performance in complex and uncertain environments, yet ensuring predictable operation under distribution shifts, sensor noise