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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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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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the Finnish Center of Excellence in Quantum Materials . Your role and goals The research will focus on developing and using machine learning algorithms to discover novel materials and to build generative models
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electrolyser systems. As well as conduct and supervise experiments particularly with PEM dynamic operation. You would work in collaboration with electrolyser modellers in Aalto, but your focus would be
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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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-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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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