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Field
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aim to develop methods to quantify such spatial arrangements. We now seek an outstanding postdoc to lead our efforts. Why join us? You will continue to develop your expertise in computational
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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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, 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