195 web-programmer-developer-"https:"-"UCL"-"U"-"https:"-"https:"-"https:" positions at ETH Zurich
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that make biomedical and clinical data usable in practice. The team currently has 5 members and supports a broad portfolio, ranging from internal tools and research platforms to applications developed
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interested in applied machine learning and computer vision at the intersection of research and industrial deployment. Job description Develop and implement state-of-the-art computer vision algorithms
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travel behaviour data or urban mobility patterns Good German language skills (at least B2) Experience with collecting own data (e.g. questionnaires; measurements) Experience with developing networks with
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frames). Project background The work focuses on data-driven generation of structural systems. You will be involved in developing, experimenting with, and evaluating machine learning models that help
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to the practical supervision of undergraduate and Master’s students through assisting in laboratory courses and semester projects. As a Research Assistant, you will be expected to plan and conduct experiments
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engaged in development of electrochemical sensors detecting environmental pollutants, providing real-time information for effective management. Past and current work includes electrochemical sensors
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G. M. Silva, within the group of Prof. Roman Stocker, and focuses on the development and deployment of a new‑generation underwater imaging device to transform drift studies in riverine ecosystems. It
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develop predictive tools for mechanical failure. Our team is highly interdisciplinary and international, bringing together researchers with backgrounds in materials science, mechanics, and applied physics
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Dr Daniel A. Richards and Professor Andrew J. deMello, in the Institute for Chemical and Bioengineering at ETH Zürich, are searching for two postdoctoral researchers to develop a diagnostic device
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, biocompatibility or the ability to reach deep brain areas. To solve this problem, we developed Ultra-Flexible Tentacle Electrodes (UFTEs), consisting of fibers one order of magnitude smaller than hair (2.4 um x 7 um