82 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" PhD positions in Switzerland
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sustainabilityIn line with our values , ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity
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technologies. You are highly motivated to learn and apply new product design and experimental techniques and collaborating with scientists from other fields (biochemistry, hygiene and medicine). The ability
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multiphysics computer models for industrial applications (e.g. in the field of hydrogen technology, photovoltaics or coupled-physics modelling). Together with its partners from science and industry, the ICP
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developing multiphysics computer models for industrial applications (e.g. in the field of hydrogen technology, photovoltaics or coupled-physics modelling). Together with its partners from science and industry
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Company-Video Where to apply Website https://academicpositions.com/ad/swiss-federal-institute-for-forest-snow-and-la… Requirements Research FieldEngineeringYears of Research Experience1 - 4 Research
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considered, please submit it by 15th November 2025. For further information, please contact Dr. Elena Jovanovska (elena.jovanovska@unibas.ch ). Where to apply Website https://academicpositions.com/ad
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with the Swiss National Science Foundation regulations for doctoral candidates (https://www.snf.ch/media/en/yXApuFw4ml0TPYe2/Annex_XII_Ausfuehrungsregl… ) for the time of their PhD (36 + 12 months
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(Bio-inspired) unsteady vortex formation and interaction More information about the lab and the ongoing and past projects can be found here: https://www.epfl.ch/labs/unfold/ ; Main duties and
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self-aware, autonomous scientific researcher. Free German courses available for those wishing to learn Salary and Benefits: Gross living allowance: ca. CHF 6’144 per month Mobility allowance: ca. CHF 664
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore