192 data "https:" "https:" "https:" "https:" "UNIVERSITY OF LUXEMBOURG" positions at ETH Zurich
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for such purposes in a wide spectrum of industries, with significant breakthroughs in computer vision, natural language processing, and intelligent control. This PhD project aims to develop foundation models (FMs
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lab / external, we are seeking a post-doctoral researcher to join our team. In the project, we will be analyzing ride-sharing, shared e-scooter and shared bicycle behaviour. We will analyse the data
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We look forward to receiving your online application including a: CV publication list statement of research interests and the names and contact information of at least two references. Please note
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, including data management and the maintenance of computational infrastructure and software. Project background The position is part of the Institute of Microbiology at ETH Zurich and closely linked
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range of topics related to imaging with geophysical data. Our research focuses on mathematical methods for processing, imaging, and inversion of geophysical data, the physics of wave propagation, and the
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, Biostatistics, Software Engineering, Systems Operations, and Screening & Lab Automation. Embedded in this multi-disciplinary environment, the Software Engineering group builds data-centric software products
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stakeholders. Ensure that information is collected, structured, and communicated to effectively inform decision-making Frame funding instruments and initiatives (e.g., Turbo Grant , WP6 umbrella project, Boost
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Academic transcripts A short statement of motivation and research interests Contact information for three references Please note that we exclusively accept applications submitted through our online
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persons Further information about Neurotechnology group can be found on our website : Questions regarding the position should be directed at ntjobs@ethz.ch (no applications). Please submit the application
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data, spanning from whole-genome variant calling and decision support to single-cell sequencing analysis. We develop and extend robust analysis workflows and reusable components, in close collaboration