18 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"P" Postdoctoral positions at Aarhus University
Sort by
Refine Your Search
-
Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater
-
Mechanics and Turbulence” group and conduct research on data-driven techniques for turbulence modeling in LES and RANS. The initial contract will be for one year, with the possibility of an additional one
-
unified data framework for microbial carbon dioxide conversion, integrating data from methanogens, acetogens, and hybrid projects for standardization, kinetic/thermodynamic measurements, and predictive
-
underlying greenhouse gas fluxes Support training of young researchers in using biogeochemical observations and data analysis Write and contribute to international peer-reviewed publications Contribute
-
will be part of a research environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental
-
departments. Contact information For further information, please contact: Dr., Peter Zeller, peter.zeller@mbg.au.dk Deadline Applications must be received no later than 23 February 2026. Application procedure
-
Aarhus University with related departments. Contact information Before applying or for further information, please contact: Associate Professor Aurelien Dantan, +4523987386, dantan@phys.au.dk . Deadline
-
) outlining a research project addressing the history of Danish botany and the Flora Danica volumes in the period 1840–1900 within the statement of future research plans and information about research
-
Development for more information. About you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria Fluency in English Strong skills in
-
description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will