76 linked-data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" "https:" positions at Aarhus University
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to experimentally derived disorder models and data from methods such as total scattering/pair distribution function analysis, diffuse scattering, diffraction, solid-state NMR and electron microscopy. Work closely
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start date 1 February, 2027. Further details and application instructions are available by clicking the 'Apply' button The webpage includes brief subject descriptions and links to individual PhD stipend
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information on bachelor and master level programs offered by the department please press the relevant links: Bachelor programs Master programs You can find examples of courses here. Please submit your course
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Department of Food Science's controlled-environment testing activities in Work Package 5 of the EVOLVE project. The work will generate biological evidence linking extracellular vesicle (EV) treatments to plant
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the Administration Centre Manager, while strategic priorities, Board-related activities and obligations linked to the Novo Nordisk Foundation grant will be coordinated with the Chairperson of the NEXUS Board
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or complements our current offerings. For information on the programs offered by the department, including course descriptions, please visit the relevant following links: Please submit your course proposal along
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advanced measurement systems, instrumentation, and data acquisition solutions. Expected start date and duration of employment The position is a full-time permanent appointment from 1 January 2027 or as soon
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The Department of Electrical and Computer Engineering at Aarhus University invites applications for a full-time, nine-month Research Assistant position in its Group of Integrated Photonics. Based in
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chemistry, physics, nanoscience or equivalent disciplines. It is a requirement that you have experience in handling ultra-high vacuum (UHV) equipment and execution of experiments and data analysis with
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are software representations of physical assets, processes, or systems. They leverage real-time data to mirror the behaviour and characteristics of their physical counterparts, enabling predictive maintenance