Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
headed by Prof. Iber, which leverages imaging data to develop data-driven, mechanistic models of biological processes. The team employs cutting-edge computational tools and imaging techniques
-
development (e.g., PyQt, Tkinter) CUDA for GPU acceleration Scientific computing libraries such as NumPy and SciPy A keen interest in scientific computing, atmospheric sciences, or advanced instrumentation is
-
/smart, confocal, super-resolution, light sheet microscopy). Knowledge of state-of-the-art image processing and analysis tools and a track record in developing innovative image analysis and data science
-
focuses specifically on using and refining the ICON model in Large-Eddy Mode (ICON-LEM) to simulate the cloud seeding experiments conducted during the project and improve process-level parameterizations
-
, computer engineering and/or computer science towards producing relevant and impactful health-monitoring mobile/wearable solutions, then please apply. The research will be highly collaborative; you should be
-
high computational cost presents a challenge for real-time process control. This project aims to overcome this limitation by integrating real-world casting data, process parameters, and finite element
-
professional ready to take ownership of key processes and tools? If this sounds like you, we have an exciting challenge for you! Join the ETH technology platform NEXUS and contribute to groundbreaking research
-
, and energy sciences, this project aims to advance the knowledge of how Earth's shallow fluid systems change over time and space. Being able to closely observe and monitor these processes is pivotal in
-
development skills to advance the existing prototype to be a robust system for 1:1 scale applications of the process. Moreover, you will have the opportunity to creatively investigate and apply new state
-
environmental data. Assist during electrofishing. Support the marking of fish. After the fieldwork, you will support video-based organism identification using computer vision and machine learning tools during