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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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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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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Your tasks: Developing optimization algorithms for massively parallel hardware architectures such as AI
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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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vulnerabilities like side-channel attacks and unauthorized access, which can compromise system integrity. Developing robust security measures within AI-enabled electronics is essential for applications in defence
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(Edge AI) enables deploying AI algorithms and models directly on edge devices. However, AI workloads demand high performance processing, large scale data handling, and specialized hardware accelerators
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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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motivated and enthusiastic and keen to push the boundaries of what is currently possible when imaging with an optical microscope. Combing the latest in optical developments with the recent surge in AI
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Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial
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a PhD student, you will develop state-of-the-art learning and inference methods to detect and characterize anomalous radio behavior and to design algorithms that remain reliable under practical