103 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr" positions at ETH Zurich in Switzerland
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reliable ground truth data for developing automated condition indicators from GPR measurements. Job description Support in planning and preparation of the experimental campaign (test track configuration
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relevant information from the papers. Our research group is dedicated to investigating human cognition and learning processes. We conduct theory-driven research about how people learn and how to develop
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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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for the physically informed processing and joint analysis of time-series data. The successful candidate will conduct observational programmes with the SPECULOOS facility and actively participate in the scientific
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of enrollment Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered. Further information about the
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Required Experience: PhD in Geodesy, Geomatics, Aerospace Engineering, Signal Processing, or a related field Proven experience in GNSS data analysis and processing Very good programming skills (e.g., Python
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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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, 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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60%-80%, Zurich, fixed-term Are you an ambitious data scientist with strong analytical and numerical skills, and expertise in geomatics, remote sensing, and data processing? We invite you to join
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industrial partners to tackle advanced modelling, simulation, sensing, and data analysis challenges in engineering systems across sectors. Project background The COMBINE Doctoral Network aims to train a cohort