356 machine-learning-"https:" "https:" "https:" "https:" "https:" "UCL" positions in Switzerland
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- 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
- University of Zurich
- CERN
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- 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)
- University of Berne, Institute of Cell Biology
- University of Geneva
- Università della Svizzera italiana (USI)
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. The combination of biological and technological aspects is central in our group and in this project. A possible candidate should have strong disposition to learn and improve novel methods, should be very open to
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candidate should have strong disposition to learn and improve novel methods, should be very open to different research disciplines and should be able to communicate across disciplines. Good communication (in
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who share our guiding principles: Curiosity: You enjoy learning, exploring new ideas, and understanding problems deeply. Openness: You listen, collaborate, and are receptive to different perspectives
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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
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upcoming areas off the beaten paths. Our three main areas of research are machine learning, distributed systems, and theory of networks. Within these three areas, we are currently working on several projects
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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
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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
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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
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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
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-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI approaches to biological questions Collaborating closely with