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Field
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devices Develop hardware-aware machine learning models incorporating electronic and optical device constraints Design and implement hardware-efficient training methodologies for machine learning systems
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can therefore draw on the expertise of a strong network. At Fraunhofer Institute for Industrial Mathematics ITWM, we see our task as further developing key technologies, providing innovative impetus
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experimental molecular biology and data analysis. Doctoral candidates can specialize in genomic and molecular biology techniques, as well as in algorithms, statistics, and artificial intelligence for molecular
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candidates which are explored in more depth. In particular you will work on the extension, development and analysis of new quantum algorithms for near-term and fault tolerant quantum computers for drug
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Data-Driven Autonomous Mobile Robotics for Aquatic Biodiversity Monitoring Shape the Future of Field Robotics You want to develop robotic systems that do not just work in the lab, but operate robustly in
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In particular, we aim to develop a neural network architecture that will allow us
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locations in Frankfurt am Main and Berlin, DIPF develops and documents knowledge about education and thus supports science, politics and practice. The Information Center Education (IZB) department is looking
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Contribute to the development of new algorithms and methods for the efficient analysis of large-scale omics datasets Participate in workflow automation and management using systems such as Snakemake
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-driving cars or to prevent factory workers from being injured by heavy machines. While AI algorithms may achieve great accuracy in the detection of persons, it is necessary to understand, in which