348 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" positions in Switzerland
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
- 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
- University of Zurich
- Università della Svizzera italiana (USI)
- 11 more »
- « less
-
Field
-
information about the institute can be found on our website and about Prof. Dr. Eva Heinen . Questions regarding the position should be directed to Prof. Dr. Eva Heinen eheinen@ethz.ch (no applications). About
-
knowledge and technology from research to Swiss machine, electrical and metal industries. The research group Laser Material Processing at inspire offers in collaboration with the Advanced Manufacturing
-
our neural implant technology market-ready for commercialization The full lifecycle where you don't just design and build devices, but you also get to see them in action with in vivo brain-machine
-
discovery, and machine learning. In the wake of quantum mechanics' initial breakthroughs, we're on the brink of a second quantum revolution. Quantum physicists are adopting machine learning to explore complex
-
methods. Contributing to the development, adaptation, and application of machine‑learning models tailored to RODI data (in collaboration with project partners). Designing and implementing an innovative
-
-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI approaches to biological questions Collaborating closely with
-
incorporating machine learning. 2. Transcriptome Recording and Cellular History Reconstruction We are advancing our CRISPR-based transcriptional recording method (Schmidt, Nature, 2018; Tanna, Nature Protocols
-
to positive change in society You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits
-
space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where conventional
-
) or equivalent in civil, mechanical or electrical engineering, geosciences, physics, applied mathematics, computer sciences or related fields, and be at the beginning of their research career. Principal