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The Department of Physics currently has about 100 employees and conducts research in areas such as Material Science and Nanotechnology, Atomic/Molecular and Optical Physics, Complex Systems and
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-throughput computational screening methods for alloy design, experimental alloy production (casting and/or AM), testing and characterisation of the thermo-physical and mechanical properties of the designed
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), testing and characterisation of the thermo-physical and mechanical properties of the designed materials, and benchmarking against existing solutions. The project is conducted in close collaboration with
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are looking for a postdoctoral researcher with a strong background in speech synthesis and machine learning, with an interest in accessibility and human communication. Your Role You will: Plan and lead
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lead a work package in our ERC Synergy grant (D2Smell) with the overall aim to digitalize smell. The aim of this sub-project is to develop and validate a new method to record electrophysiological
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develop novel electrodes and electrolytes for high-performance aqueous iron- and/or zinc-ion batteries. The work involves studying electrochemical energy storage and degradation mechanisms of electrode
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coupling where applicable. The present post-doc position will contribute and lead research in relation to the development and validation of techno-economic performance models for the design and operation of
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energy community deployment. The researcher will lead activities related to time-resolved energy systems modelling, optimisation, and digital twin development for pilot sites, including scenario-based
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lead advanced statistical analyses integrating ecological datasets with spatiotemporal modelling frameworks. The work will contribute with evidence-based data to development of ecosystem-based
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candidate will lead advanced statistical analyses integrating ecological datasets with spatiotemporal modelling frameworks. The work will contribute with evidence-based data to development of ecosystem-based