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on futuristic technologies in the field of machine learning and computer vision. Hence, we investigate and develop an innovative computation-in-memory (CIM) solution for artificial intelligence accelerator design
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»Integrated Circuits and Systems« at Fraunhofer Institute for Integrated Circuits we develop customized solutions in the field of “Integrated Circuits and Systems” with the focus on mixed-signal ASIC design for
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review on existing EIS probe systems, contact design strategies, and integration approaches for automated testing. Design and model a probe system for reliable electrical contact with different lithium-ion
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sensors based on the integration of organic light emitting diodes (OLED) onto silicon CMOS substrates. Here, our activities range from the initial idea, through design in close cooperation with the OLED
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PhDGermany Find your suitable PhD opportunity in Germany. Find your suitable PhD opportunity in Germany. Back to Overview Working LanguageEnglish LocationJülich Application Deadline07 Nov 2025 Starting Dateas soon as possible Content Navigation Overview Description Required...
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and circular processes, enabling used products to be transformed into new generations. What you will do Design and implement a sampler for Gaussian Mixture Models (GMMs) Investigate and compare
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of embedded machine learning, neuromorphic hardware and deep learning accelerators. Want to get more information? Click here. What you will do Responsible for RTL design (VHDL, Verilog) of digital blocks and
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Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Promotion Job description: Your Job: Digital methods for inverse materials design are essential
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Your Job: Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so
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oxide. These are used beyond traditional storage applications, including AI applications. To design a full memory array and use it for AI applications, high-performance peripheral circuits are needed. As