Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
, 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
-
properties. In parallel, they continuously sense and respond to diverse mechanical cues from their environment, including adhesion, stiffness, tension, shear, pressure, and confinement. These cues
-
of computer graphics fundamentals, numerical methods, and GPU/parallel computing concepts. Experience with at least one major deep learning framework (PyTorch preferred). Excellent problem-solving skills and
-
100%, Zurich, fixed-term We have an open PhD position at the intersection of machine learning, embedded intelligence and human–computer interaction. The project will explore how learning systems can
-
applications. HPC and orchestration of scientific data processing workflows. Parallel computing (GPU & CPU). good software engineering practices for scientific software (version control, testing, continuous
-
and outbound prospects related to any outreach and sales activities for SNAI. Devise and implement sales formats enabling multiple prospects to engage with SNAI offerings in parallel (thematic events
-
energy transfer, developing and employing computer simulations, laboratory experiments, and field analyses. Our aim is to gain fundamental insights and develop sustainable technologies to address societal
-
through the EU Research Framework Programme? Horizon Europe - MSCA Is the Job related to staff position within a Research Infrastructure? No Offer Description This doctoral position is offered by the Wood
-
COMPAS XR framework developed at ETH Zürich. Project background The successful candidate will work at the intersection of computational design, XR, human-computer interaction, and robotic fabrication, with
-
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