159 parallel-computing-numerical-methods-"Prof" positions at ETH Zurich in Switzerland
Sort by
Refine Your Search
-
found on our website . Questions regarding the position should be directed to Prof. Dr. Elizabeth Tilley by email (no direct applications). About ETH Zürich ETH Zurich is one of the world’s leading
-
computational methods, artificial intelligence (AI), and extended reality (XR). Design++ fosters cross-departmental research, education, and industry collaboration to drive transformative change in the built
-
/ computational sciences or related field Good understanding of the mathematical and numerical foundations of machine learning, statistical analysis and modeling Proficiency in Python and its ML tech stack
-
devices from motors and actuators to sensors, memories, and emerging paradigms for computation. Despite their technological importance, many fundamental aspects of their behavior remain elusive, especially
-
. Scientific IT Services (SIS - a section with ITS) aims at bridging the gap between computational research and IT services and infrastructure provisioning. We are working closely together with ETH researchers
-
experience in developing software for scientific applications, data analysis, or real-time systems is desirable. Experience with parallel computing and optimization techniques for handling large datasets
-
more comprehensible for you. The Research Analytics Services group, hosted by the section Research Support Services and Acquisitions, is hiring a specialist in library and information science as team
-
application portal. Applications via email or postal services will not be considered. Questions regarding the position should be directed to Prof. Jörg F. Löffler (joerg.loeffler@mat.ethz.ch) or Dr. Robin E
-
have a master’s degree in atmospheric and/or climate sciences, geochemistry, environmental chemistry, physics, computational sciences or a closely related discipline with a strong interest in numerical
-
technologies promise to revolutionize multiple branches of science by solving problems that cannot be tackled by classical systems. While efficient and large-scale quantum computers are still far from being