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Max Planck Institute for Biological Intelligence (Seewiesen site), Seewiesen | Germany | 3 months ago
Job Offer from July 06, 2026 The Neural Mechanisms of Natural Behavior Research Group at the Max Planck Institute for Biological Intelligence in Germany has an opening for a pre- or postdoctoral
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position within a Research Infrastructure? No Offer Description Area of research: Scientific / postdoctoral posts Job description:Postdoc - scalable, shuttle-based spin-qubit quantum-chips The JARA-Institute
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Investigator (PI) Prof. Newig, one postdoctoral researcher, and four doctoral researchers, each focusing on one analytical stream: (1) sustainability performance, (2) democracy, justice, legitimacy, (3) global
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quantitative live-cell imaging to probe and model these processes. By combining stem cell biology with cutting-edge microscopy and physical concepts, we aim to establish a predictive framework for tissue self
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cell handling, differentiation, and organoid formation, while inte-grating automated imaging, quality control, and data analysis. A strong component of the work will be the programming and customization
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(e.g. via machine learning) to qualitative analyses (e.g. via interviews) to support ambitious policies for climate and energy transitions. This position Green hydrogen is key to decarbonizing many hard
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childcare allowances are provided. Learn more about Working at HITS. Eligibility and requirements Candidates must hold a doctoral degree or an equivalent academic qualification at the beginning of
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of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning
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Max Planck Institute of Microstructure Physics, Halle (Saale) | Halle, Sachsen Anhalt | Germany | 2 months ago
programming and numerical modeling Candidates do not need to have experience in all of these areas. Applicants with a strong background in electronic-structure calculations and an interest in learning quantum
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exploration and optimization of the process parameter space, as well as for adaptive, data-driven machine learning approaches to map the electrolysis process to a digital twin. In parallel, data workflows and