Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
journey, from the collection and management of data to machine learning, AI, and industrialization. With a large multidisciplinary team of professionals across three locations (Lausanne, Zurich, Villigen
-
) Contribute to the strategic direction of research Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning Collaborate with
-
applying machine learning to PNT (Positioning, Navigation, and Timing) and geomonitoring challenges, including signal characterization and anomaly detection. Project background We are looking for a highly
-
(multi-view RGB imaging), drones, handheld and manual devices Contribute to the design and/or establishment of a phenotyping robot that can acquire data from RGB cameras and potentially other sensors
-
methods and approaches are needed to better tackle the challenges posed by increased uncertainty and complexity. Machine learning (ML) and artificial intelligence (AI) methods have shown promise
-
devices Contribution to the design and/or establishment of a phenotyping robot that can acquire data from RGB cameras and potentially other sensors Improving phenotyping workflows and models to extract
-
Systems.”Funded through an ETH Zurich Career Seed Award, this project aims to develop scientific machine learning frameworks that integrate physics-based modeling with neural network architectures. The goal
-
received by 1 November 2025. Note: as an option, one of the four reference letters can be about teaching. For more details, please see the website: https://math.ethz.ch/fim/postdocs.html Application
-
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
-
dynamical systems, and machine learning, with applications to synthetic biology and biomolecular circuit design. Our research develops mathematical and computational frameworks for understanding and