Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
contribute to the development and optimization of the training pipelines for these large-scale models, working at the intersection of cutting-edge research and high-performance computing to advance
-
engineering, computer science, data science, or a closely related discipline Have an excellent academic record Have strong analytical skills Be passionate about sustainability, energy, and public policy Be
-
at ETH Zurich and associated with the ETH AI Center. We are an interdisciplinary group at the intersection of chemistry and computer science. Our mission is to accelerate chemical discovery using digital
-
, electrical engineering and computer science to design highly efficient and sensitive imaging and inference approaches to help guide diagnosis and treatment in cardiovascular patients. Project background Our
-
Emulators of Stochastic computational models"), funded by the Swiss National Science Foundation (SNSF). The project aims to significantly advance the state-of-the-art in uncertainty quantification (UQ) by
-
. Experience with running numerical models is an advantage. Experience in working in a Linux/Unix high-performance computing environment is a plus. Good communication skills in English (written and spoken
-
. Integrate various datasets, such as tree species annotations, climate, and topography, into deep learning algorithms. Test deep learning models (Transformers and CNNs) for optimal accuracy using large
-
experience in statistical analysis and implementing machine and deep learning models using Keras/TensorFlow and/or PyTorch. You have experience in collaborative coding, version control, and utilizing computer
-
(IVT) at ETH Zurich intends to develop scalable optimization systems for operational support in large-scale road networks. Modeling and simulation are powerful tools for the development and validation
-
of computational and statistical genomics, and bioinformatics. Cattle are an interesting «model organism» to study inherited genetic variation and the molecular-genetic underpinnings of complex traits and dieseases