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considered an asset in these positions. Applicants must also be able to demonstrate excellent ability to code with or learn computer programming languages, such as C++, C#, Python, and/or Matlab
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modelling, preferably WRF Experience of scientific programming and running code on HPC systems Experience with Fortran, Python and Linux Shell Any of the following is advantageous but not essential
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Steven Ludeke to discuss their expected degree timeline. Highly proficient in at least one statistical programming language (e.g., R, Stata, SAS, Python). Candidates that can show an aptitude for learning
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related field Experience with Earth System or biogeochemical modelling Strong programming skills (e.g., Python, R, MATLAB) and experience with advanced machine learning modelling A strong interest in
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machine learning, statistics, time series analysis or similar field Demonstrated ability to conduct outstanding research with measurable impact Experience with Python, especially related to machine learning
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, ideally in Python. Experience with X-ray or neutron tomography data is a plus. Flexibility is essential. We are looking for a team player who can also work independently, who is motivated to help and be
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applications (Python, C/C++, or related languages) Further information about this position is available via email from Assistant Professor Mubashrah Saddiqa (msad@mmmi.sdu.dk ) or Torben Worm (tow@mmmi.sdu.dk
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, Python and QGIS Good knowledge of geology and hydrogeology in aquitard-aquifer systems Experience in collaborations with partners from consultancy and public authorities Ability to work both in a team and
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environment research experience working with real-world non-linear dynamic data, networks, and ideally with applying coupled oscillator models strong programming skills in e.g., Python previous research
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developed python-based EM forward operator. Contributing to the development of a freeware software package that offers both forward and inverse modeling capabilities for FEM and TEM data. Collection of FEM