Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- ETH Zurich
- University of Basel
- ETH Zürich
- Empa
- Nature Careers
- Paul Scherrer Institut Villigen
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL
- EPFL - Ecole Polytechnique Fédérale de Lausanne
- HES-SO Genève
- University of Zurich
- CERN
- EPFL
- Ecole Polytechnique Federale de Lausanne
- Friedrich Miescher Institute for Biomedical Research
- Graduate Institute of International and Development Studies, Geneva;
- Idiap Research Institute
- Inselspital Bern
- Physikalisch-Meteorologisches Observatorium Davos (PMOD)
- School of Architecture, Civil and Environmental Engineering ENAC, EPFL
- University of Geneva
- Università della Svizzera italiana (USI)
- 11 more »
- « less
-
Field
-
understanding of the aging of solid insulation under mixed-frequency medium-voltage stress, see https://doi.org/10.1088/1361-6463/acd55f for a relevant example research work of our team in this area. Profile
-
the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
-
knowledge and technology from research to Swiss machine, electrical and metal industries. The research group Control and Automation at inspire AG offers the following position in collaboration with Bota
-
. • Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling. • Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.
-
-structure interactions of flapping flags (Bio-inspired) unsteady vortex formation and interaction More information about the lab and the ongoing and past projects can be found here: https://www.epfl.ch/labs
-
Computer Vision and Computer Graphics techniques to digitize human avatars and garments in 3D. Within this project, your role is to advance our existing algorithms that reconstruct 3D garments from multi
-
thinking with a structured, quality-focused approach to data and methods. Ideally, experience in one or more of the following: data engineering, building data-driven apps, computational linguistics, machine
-
of machine learning, AI, and cancer genomics. Our lab develops novel machine learning methods to understand biological systems and cancer, with a strong focus on genomics and translational impact. We work in
-
experience working in collaboration with biological or clinical labs and with groups with a strong machine learning background. The starting date is by mutual agreement. We expect a pronounced interest in
-
the environmental drivers that regulate these processes. We will use machine learning approaches (XGBoost, SHAP analyses) for the flux partitioning, complemented by existing tree dendrometer and sap flow measurements