Sort by
Refine Your Search
-
(ideally 1 page, maximum 2 pages) containing: Statement of research interests, past and future, 1 paragraph summary of MSc thesis research and Career goals. CV Copy of certificates Contact information for 2
-
services will not be considered. Further information about LESE can be found on our website . Questions regarding these positions should be directed to Prof. Christoph Müller muelchri@ethz.ch or Dr. Paula
-
email or postal services will not be considered. Further information can be found on our website . Questions regarding the position should be directed to Prof. Dr. Sascha Quanz (sascha.quanz@phys.ethz.ch
-
of the center. Contact information for two references, which can be appended to the CV or provided as a separate PDF if you prefer. No reference letters are needed at the time of application, and we will notify
-
. Applications sent via email or postal services will not be considered. Further information about the lab can be found on our website . For questions about the position, please contact Ms. Eva Skalamera
-
considered. Further information about Veltst can be found on our website . Questions regarding the position should be directed to Dr. Alexandre Anthis, email aanthis@ethz.ch (no applications). We would like
-
-assisted magnetic recording is a breakthrough technology that integrates a tiny laser and nanoantenna directly into hard drives, pushing the limits of data storage density and paving the way for the next
-
considered. Further information about our Department can be found on our website , consult our published work . Questions regarding the position should be directed to Professor Martin Fussenegger
-
application portal. Applications via email or postal services will not be considered. Further information about our Department can be found on our website , consult our published work . Questions regarding
-
learning (RL), such as (but not limited to) Theory of online learning, reinforcement learning, and data-driven control Learning in games, and multi-agent RL RLHF and alignment in LLMs Representation learning