15 parallel-and-distributed-computing Postdoctoral research jobs at ETH Zurich in Switzerland
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on the development of an Urban-scale Building Life-cycle Carbon Calculator in the form of a web app using relevant open-access technologies such as Cesium ion. This carbon calculator web app will combine
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100%, Zurich, fixed-term The Engineering Design and Computing Laboratory is offering a fully-funded post-doctoral position to work on cutting-edge research projects in AI-based computational design
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optic cable can replace thousands of electrical links to transmit the same amount of information. As parallels to the thriving field of stretchable electronics, soft optics is emerging as a new platform
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may be agreed, e.g. due to family constraints or parallel employment or self-employment in an architectural design office. Please indicate your reasons in case you apply for a part-time employment
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directly atop the CMOS chips. Job description Therefore, it will be necessary to develop technologies and methods to minimize or avoid light-induced artifacts on the HD-MEAs, to establish and program a setup
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modelling and/or empirical analysis. The research will ideally combine insights from economics and e.g. computer science. Profile Applicants should hold a PhD in Economics with a strong economic basis and
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the field of Computer Science or similar Solid background in the foundations of reinforcement learning Proven research experience with first-authored publications at peer-reviewed conferences (ICML
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candidate with a strong background geology/geomorphology, or a related discipline, a strong interest for evolutionary biology, and who is interested in bridging field data, computational modeling, and large
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imaging, single cell tracking and quantification, large volume 3D bone marrrow imaging with single molecule sensitivity, and ai-supported computational analysis. Job description We are seeking a highly
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PhD in Computational Materials Physics or a related area is required. Experience with electronic structure calculations is essential. Familiarity with the use of machine-learning tools in materials