133 computer-"https:"-"https:"-"https:"-"https:"-"IRIBHM-ULB" positions at ETH Zurich
Sort by
Refine Your Search
-
, Switzerland [map ] Subject Areas: Computer Science / Distributed Systems and Networking , Networking , Networking and distributed systems Appl Deadline: 2026/01/08 11:59PM (posted 2025/11/10, listed until
-
-facturing processes. In this internship, you will work on state-of-the-art anomaly detection methods using computer vision and time-series data, with a particular focus on multimodal data fusion for powder
-
for key program deliverables. Drive the development of high-impact deliverables, such as the annual report and the Phase III Outline Proposal. Coordinate input across projects, synthesize insights, and
-
psychologists, neuroscientists, computer scientists, clinicians, and data scientists across the Singapore-ETH Centre (SEC), the National University of Singapore (NUS), and Nanyang Technological University (NTU
-
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
-
, neuroscientists, computer scientists, clinicians, and data scientists across the Singapore-ETH Centre (SEC), the National University of Singapore (NUS), and Nanyang Technological University (NTU), the PhD student
-
100%, Zurich, fixed-term The Clinical Genomics team led by Dr. André Kahles at the Biomedical Informatics Lab (BMI Lab), headed by Prof. Gunnar Rätsch, at ETH Zurich, is seeking a highly motivated
-
operations that are yet to be fully understood. In this context, it is evident that the operation, control, and planning of power systems will soon be pushed to their limits. Therefore, new computational
-
organised individual to support multiple experimental and computational research groups. The successful candidate will contribute to both a collaborative research network and institute-wide activities
-
100%, Basel, fixed-term The Computational Biology (CoBi) group, led by Prof. Dagmar Iber, develops data-driven, mechanistic models of biological systems using advanced imaging and computational