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center ScaDS.AI Dresden/Leipzig (Center for Scalable Data Analytics and Artificial Intelligence) is being expanded into a leading German AI competence center for Big Data and Artificial Intelligence (AI
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) Positions PhD Positions Country Germany Application Deadline 21 Sep 2025 - 23:59 (Europe/Berlin) Type of Contract Temporary Job Status Other Job Status Extra Information The position is limited to 3 years Is
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available on site for the development of suitable radiotracers. One focus of the work is on the use and evaluation of large tomographic data sets to derive parameter data for reactive transport modeling
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the smooth processing of big data in the context of scientific, AI-driven energy systems research – scalable, transparent, and interoperable. Your tasks in detail: Development of a structured description
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of the PhD Positions:•Multidisciplinary training: Training of DCs will exploit multidisciplinary consortium expertise spanning design, modelling and simulation of photonic systems, sensor systems, signal
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Your Job: As a PhD candidate you will develop and deploy an artificial intelligence (AI) driven approach to streamline high-throughput experimentation (IMD-3: Institute of Energy Materials and
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highly motivated PhD student to join a DFG-funded international project that investigates plant - microbiome interactions through large-scale metabolomics and other - omics platforms. Your tasks: Process
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Your Job: The PhD candidate will be in charge of: The development of photopolymerizable hydrogel blends with n-type and p-type semiconductor capabilities The pattering of the semiconducting hydrogel
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solving. Diverse research areas: Work on numerical models, analyzing large data sets, statistical methods and more in a unique scientific environment. Who Should Apply? Emphasizing the physical system
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 22 days ago
architecture of important crop traits like grain yield heterosis. In the era of large population size and dense genomic data such as whole-genome sequencing, new algorithms are needed to remove the bottleneck