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currently consists of 3 permanent staff and a shared technician. Your role We are looking for a postdoc with a background in magnetic materials and (soft) x-ray techniques, like XMCD or magnetic X-ray imaging
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experiments, and machine learning (ML) to understand and predict multiscale transport phenomena in fuel cell systems. In particular, the postdoc will bridge pore-scale simulations and macroscale performance
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illuminate and understand various aspects of human development over the past 10,000 years, thereby creating a comprehensive picture of human prehistory regarding language, culture, and genetic evolution in
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extent to which large-scale, reactivated basement structures focused mineralization. The focus is on regional-scale three-dimensional (3D) anisotropic magnetotelluric (MT) imaging of the deep crust and
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for The following requirements are mandatory: To qualify for the position of postdoc, you must hold a doctoral degree in materials science, physics, chemistry, nanotechnology, or a related field awarded no more than
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extent to which large-scale, reactivated basement structures focused mineralization. The focus is on regional-scale three-dimensional (3D) anisotropic magnetotelluric (MT) imaging of the deep crust and
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that facilitate interactions of electrons and spins could revolutionize information processing and storage technologies. Towards this purpose, we aim to explore low-dimensional magnetoelectric and altermagnetic
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fluid dynamics and vascular modeling in microenvironments Skills in data analysis and image processing (e.g., Python, R, ImageJ) Ability to mentor junior researchers and contribute to team leadership What
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to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven
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, electrophysiology, chemo-optogenetics. Behavioral measurements will be performed in collaboration with Olof Lagerlöf’s group. The postdoc will image (and influence with opto-chemogenetics) the network representation