21 algorithm-"EPFL"-"INSAIT---The-Institute-for-Computer-Science" positions at ETH Zurich
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counterpart at EPFL, establish strategic partnerships, and manage communication channels to advance CLUE's mission of bridging research insights with real-world educational contexts. The position is initially
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postdoctoral researcher in the field of plasmonic nanostructures. The project is a collaboration between groups at Empa, ETH Zurich and EPFL. Job description In this position, you will be responsible
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Our research group focuses on the development of AI algorithms for industrial applications. The main scope of our activities is the optimization and automation of workflows and production systems
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100%, Zurich, fixed-term The SDSC has been a National Research Infrastructure since 2025, evolving from a strategic focus area of the ETH domain, with EPFL and ETH Zurich as founding partners. Its
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generation of enhancement algorithms tailored for endoscopic videos, with a focus on applications in real-time interventions. Development efforts will prioritize both enhancement quality and computational
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-focused environment. Project background The Swiss AI Initiative is a collaborative research project led by ETH Zurich and EPFL, focused on developing responsible and transparent generative AI. A significant
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scientific questions or develop algorithms that perform automatically some challenging tasks. This typically involves exchanging actively with collaborators and domain experts to understand the precise
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. Integrate various datasets, such as tree species annotations, climate, and topography, into deep learning algorithms. Test deep learning models (Transformers and CNNs) for optimal accuracy using large
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strategic focus area of the ETH domain, with EPFL and ETH Zurich as founding partners. Its mandate is to support academic groups and research, hospitals, industry, and the public sector at large, including
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) or neural network-based methods. The level of the targeted problems will require further mathematical and algorithmic developments over the current state of data-driven SSM reduction. The PhD position will