87 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"ETH-Zürich" positions in Switzerland
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maintain infrastructure and lab equipment (e.g. microtomes, automated stainers, and tissue processors). Track and manage samples and data with precision. Develop new protocols and methods. Implement quality
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. The project will address questions regarding inheritance flows, inter-vivos gifts, bequest motives and taxation. You will be involved in all stages of research, from cleaning data to carrying out data analyses
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to efficiently train on the large, densely-connected and graph-structured data encountered in our systems of interest. Your contributions would be across the spectrum from methodological development
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, you process collected data, prepare reports, and deliver results back to customers and internal teams - ensuring that every project is both professionally executed and well-documented. Further, you will
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vision and its associated diseases, as well as to develop new therapies for vision loss. For more information, please visit iob.ch ! About the project For a project in the Human Retinal & Central Visual
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multimodal data analysis, with an initial focus on neuroscience. To build this interdisciplinary platform, we invite applications for three PhD-level Research Specialists: Microscopy and Spatial
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Health (AICH) group develops AI/ML methods, digital tools, and secure data pipelines to advance pediatric healthcare. We work at the intersection of clinical medicine, machine learning, and data-intensive
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problems in scientific or engineering domains using proprietary/real data (beyond public benchmarks), where challenges like distributional generalization, multi-objective trade-offs, causality, privacy
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The primary focus of this position is the project-specific analyses of diverse high-throughput multi-omics datasets, encompassing a broad range of data types such as whole-genome and transcriptome
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-focused , contributing primarily to one of the following projects: Project A – Synthetic Data for Theory-Driven Behavioral Research This project investigates how large language models (LLMs) produce