Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- ETH Zurich
- University of Basel
- Empa
- ETH Zürich
- Swiss Federal Institute for Forest, Snow and Landscape Research WSL
- Nature Careers
- University of Zurich
- Academic Europe
- EPFL - Ecole Polytechnique Fédérale de Lausanne
- EPFL FSB
- CERN
- EPFL
- EPFL - Ecole polytechnique fédérale de Lausanne
- ETH ZURICH
- University of Applied Sciences Northwestern Switzerland
- University of Bern
- Universität Basel
- Université de Genève
- 8 more »
- « less
-
Field
-
Please submit the following documents with your nomination: Application form with details of the nominees and the nominator A title (max. 150 characters) and a brief summary of the achievement (max. 400 characters) A description of the achievement and the main results (max. 4’000...
-
: Lightweight, real-time models using quantization, compression, and adaptive execution. Hardware–algorithm co-design: Cross-layer optimization aligning ML algorithms with embedded hardware. Distributed and
-
develop cutting-edge algorithms and AI-based solutions for data processing and validation and provide scientific expertise for the implementation of future remote sensing missions. The team’s work bridges
-
processing and analysis. The LISA ground segment is a distributed facility (the LISA Distributed Data Processing Center), for which LISA member states contribute national centers with specific commitments and
-
experience. Strong programming skills in Python and experience with modern ML stacks (PyTorch, HuggingFace, distributed training). Experience in LLMs, vision–language models, multimodal learning, or clinical
-
, algorithms, AI) in society. We are in particular looking for candidates who have interest and experience with STS and humanities pedagogy in the context of a technical university and in developing research and
-
earth observation technologies. Our researchers develop cutting-edge algorithms and AI-based solutions for data processing and validation and provide scientific expertise for the implementation of future
-
. • Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling. • Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.
-
challenge. The ESA-funded LISA will rely on an extensive ground segment for data processing and analysis. The LISA ground segment is a distributed facility (the LISA Distributed Data Processing Center
-
, you will design, prototype, and optimize advanced simulation algorithms—particularly in the domain of cloth and deformable materials and contribute to our next generation of rendering and learning-based