217 developer-"https:" "https:" "https:" "UCL" positions at ETH Zurich in Switzerland
Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
-scale geospatial and Earth system datasets, within the NCCR CLIM+ program. The role bridges climate science and AI, developing novel methods for climate data analysis, downscaling, and synthesis using
-
The successful PhD candidate will contribute to the scientific development, implementation, and evaluation of a mobile app. Working within a highly multidisciplinary team of psychologists
-
to integrate cutting-edge multi-sensor fusion and AI technologies? Join our young and dynamic team at the Chair of Space Geodesy , led by Prof. Benedikt Soja . We are at the forefront of developing innovative
-
for such purposes in a wide spectrum of industries, with significant breakthroughs in computer vision, natural language processing, and intelligent control. This PhD project aims to develop foundation models (FMs
-
, are rare. This project exploits the unique monitoring capabilities of the Bedretto Underground Laboratory to investigate these processes and to develop experimentally validated guidelines for safer shut-in
-
develops uniquely interdisciplinary design and history and theory research for landscape architecture. Through geohistorical practice, we integrate tools from natural sciences and humanities with those
-
innovative methods to leverage machine learning for numerical weather forecasting and climate modeling. Project background We are looking for a motivated Machine Learning Scientist to join the development team
-
of statistical seismology and machine learning. This position is part of two EU-funded projects: GeoTwins , which focuses on creating digital twins for geothermal systems, and Earth-AID, which aims to develop
-
stimulation techniques. The goal is to develop safe, efficient, and environmentally acceptable stimulation strategies for geothermal applications across Europe. Job description Plan, conduct and analyse
-
As part of the MALOQ project, you will train state-of-the-art ML models to learn atomic, electronic, and vibrational properties of large-scale atomic systems representing the building blocks