186 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" uni jobs at ETH Zurich in Switzerland
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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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public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits chevron_right Working, teaching and research at ETH Zurich We value
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component of solid-state transformers (SSTs). Such SSTs are required, for example, in future AI data centres, where power consumption per computer rack increases to levels of several hundred kilowatts or even
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benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits chevron_right Working, teaching and research at ETH
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for highly motivated, committed, creative and eager to learn individuals, able to work in a team and with excellent communication skills. Working in a top-level research environment with advanced laboratory
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on the day-ahead market. New portfolio bidding strategies. This can include learning from previous simulation runs to reduce the portfolio's final imbalance. Utilizing real failure/maintenance rates of power
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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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with academic institutions and hospitals. 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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. 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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) distributional generalization, transfer learning, causality Multi-objective settings and alignment, RL theory Statistical learning theory, optimization (e.g., implicit bias) Robustness (broadly defined), privacy