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and Sobolev-type spaces (with Hytönen and/or Korte), Conformal deformations of metric measure spaces and/or general regularity and convergence for graph-based machine learning using stochastic game
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or microscopy-based methods for analyzing material degradation (e.g. FTIR/Raman microscopy, SEM) Experience with grant writing, project management, or coordinating collaborative research Interest in
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
cutting edge machine learning techniques for sequential data modelling, including Physics-informed Machine Learning and Koopman Operator-based representation framework, towards building interpretable
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
for sequential data modelling, including Physics-informed Machine Learning and Koopman Operator-based representation framework, towards building interpretable predictive models for complex multi-physics dynamical
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-change impacts on biodiversity and climate feedbacks, as well as the effects of upcoming large-scale restoration efforts. The postdoctoral researcher will be based in the PeatResC research team led by
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expertise, and ability to combine the particle-based approaches with statistical mechanics and continuum scale modelling of soft materials. The candidate should have demonstrated expertise in dissemination
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researcher will be based in the PeatResC research team led by Professor Miina Rautiainen at Aalto University and will work in close collaboration with partners across this internationally recognized consortium
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is about 4000 € / month (gross), with a possible increase based on achievements. The annual workload of research and teaching staff at Aalto University is 1612 hours. The employment contract includes