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, as well as: A PhD degree in the field of renewable energy engineering (or close to obtaining one), or in a closely related field. Demonstrable experience in Python and relevant modelling tools, like
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, in coordination with PI, that investigates the governance of new funding models for creators and influencers, collect and analyse empirical data (flexible, but please specify), present research
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, with a PhD in the field of economics, (business) mathematics, (business) informatics, marketing, management; Experience in data analysis, statistical and econometric modeling; Good skills in writing
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checks. Both parts are meant to support data-driven computational modeling for applications in epidemics. For a further description of the proposed project, see this document. The Department
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We are seeking to appoint a Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling to apply and develop cutting-edge deep generative probabilistic models
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Max Planck Institute for Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | 3 days ago
. The start date of the position is October 2026, with some flexibility (to be negotiated). The position is limited to three years and according to current German labor law. Applicants must have obtained a PhD
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also be encouraged and supported to apply for independent research grants. Additional information The position is expected to start in spring 2026 or as agreed. For additional information, contact Assoc
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the Oxford–Novartis Collaboration for AI in Medicine. You must hold a PhD/DPhil in Statistics, Statistical Machine Learning, Deep Generative Modelling, or a closely related field, together with relevant
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are groundbreaking for battery management and will make substantial advances in online explainable data-driven prediction for time series data. The specific tasks of this postdoc position will be to use active
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to differentiate between healthy and accelerated brain aging; ii) create multiphysical constitutive models of cortical tissues degeneration during aging and injury; iii) utilize longitudinal medical image data