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iNANO at Aarhus University is seeking a postdoctoral fellow for the Novo Nordisk Foundation CO2 R...
. Contact info Applicants seeking further information are invited to contact Alfred Spormann, e-mail: aspormann@au.dk Deadline for application and the recruitment process The application deadline is 30th
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. The overall goal of the project is to design a novel high temperature heat pump. You will be conducting Computational Fluid Dynamic (CFD) simulations and cycle analysis, which will be instrumental
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to ensure that letters of reference received after the application deadline will be taken into consideration. If you wish to add a referee after you have submitted your application, you must send
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collaborate with PhD and other post doctoral fellows, in cross-disciplinary collaborations with other research groups both national and international as well as with industrial partners. Qualifications A PhD
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submitted your application, you must send this person’s details (name, job title, place of work, and email address) as well as the name of the position you have applied for to: HR.Nattech@au.dk Formalities
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graduate and/or post-graduate experience in time-resolved X-ray experiments and a proven record of accomplishment within state-of-the-art simulation and analysis of such data sets. We are looking for a
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formation and changes occurring during processing and digestion. The starting date is November 1, 2025, or as soon as possible hereafter. The research project This post-doctoral position is part of the EU
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. The carbon capture pilot is envisioned to include pre-treatment and post-treatment steps, enabling flexibility and optimization of the carbon capture process for its practical application. The position is
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A postdoc position in the Torben Heick Jensen lab, Aarhus University, Denmark: Mammalian Nuclear ...
computational biologists aiming to examine the factors and complexes governing the production and turnover of eukaryotic transcriptomes. The postdoc will be affiliated to the Department of Molecular Biology and
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computational models to map co-expression networks and predict systemic disease transitions. Characterise intestinal microbiome changes and their correlation with inflammatory diseases. Computational modelling