51 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" PhD scholarships in Switzerland
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dynamics simulations is highly desirable. Basic knowledge of machine learning is considered an advantage but is not mandatory. LanguagesENGLISHLevelExcellent Additional Information Work Location(s) Number
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electrocatalysts During your PhD, you will interact closely with colleagues within the electrochemistry department, the Swiss Light Source, and international collaborators Where to apply Website https
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approaches. The research will also design a European-scale turbidity and sediment monitoring framework combining in situ observations and satellite remote sensing. Where to apply Website https://jobs.unibas.ch
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with competitive salary according to ETH standards Interdisciplinary and international research environment You can expect numerous benefits , such as public transport season tickets and car sharing, a
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the position should be directed to Dr. Diana Santelia by email: dsantelia@ethz.ch (no applications). Further information about the Institute of Integrative Biology can be found on our website . We would like
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), INSERM (FR), University Utrecht (NL), SciCross (SE), RD –Néphrologie SAS (FR), University of Bern (CH). For more information https://www.cordis.europa.eu/project/id/101225380 Your Research Environment In
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systems, and space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where
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Opportunities to learn cutting edge techniques Perspectives for career development A diverse and interdisciplinary team Working, teaching and research at ETH Zurich We value diversity and sustainabilityIn line
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environment. In line with our and Uni Basel values ( https://www.unibas.ch/en/Research/Values-Ethics/Diversity-and-Inclusion.html ), we are committed to sustain and promote an inclusive culture, ensure equal
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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures