243 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" uni jobs in Switzerland
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curious to learn new things, is self-motivated, and has excellent English communication and writing skills. Knowledge of German language will be considered an advantage. Our offer You will be working in a
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a solid platform for the PhD candidate. In line with our values , ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning
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assistant at the chair, you are expected to teach a combination of landscape architectural design and research. You will need to be able to understand the student’s ideas and to help them further developing
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an internationally recognised, competitive research program sustained by external funding. Teaching experience at university level is expected. The candidate will teach at the Bachelor level and contribute
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, which not only supports your professional development, but also actively contributes to positive change in society. You can expect numerous benefits , such as public transport season tickets and car
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. Profile Applicants must hold a M.Sc. Diploma (120 ECTS points) or equivalent in civil, mechanical or electrical engineering, geosciences, physics, applied mathematics, computer sciences or related fields
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the guidance of your supervisors You must be willing to exchange with your fellow team members, support them, and learn from them. Non-traditional research topics, questions and methods are encouraged We also
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) distributional generalization, transfer learning, causality Multi-objective settings and alignment, RL theory Statistical learning theory, optimization (e.g., implicit bias) Robustness (broadly defined), privacy
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an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our
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deep learning workflows for tree species mapping. The position contributes to building a scalable system for forest monitoring by refining model performance and ensuring high quality geospatial data