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and Python are required. The successful candidate will be based in Odense, under the primary supervision of Prof. Stefan Jänicke. The appointment will be made for 2 (two) years at a competitive salary
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insight Develop and use Python tools for EIS data processing and modeling Perform microstructural and compositional analysis (electron microscopy, XRD, Raman, EDS) Contribute to fabrication of SOE cells (3D
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assessments. Key responsibilities Design and conduct experiments. Operate and maintain gas measurement equipment and flux chambers. Process, analyze, and visualize large data sets using Matlab, R, Python
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assessments. Key responsibilities Design and conduct experiments. Operate and maintain gas measurement equipment and flux chambers. Process, analyze, and visualize large data sets using Matlab, R, Python
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skills, with the ability to bridge theory and experimental data Experience with computational tools such as MATLAB, Python, and multiphysics simulation platforms (e.g., COMSOL, Ansys) Motivation to engage
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. Proficiency with CST Studio Suite, HFSS, and related full-wave EM simulation workflows. Competence in MATLAB or Python for numerical modelling, data analysis, and optimisation. Ability to conduct experimental
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. Programming experience is required (Python, R). Strong analytical, organizational, and record-keeping skills Interest in working in a multidisciplinary and multicultural team Willing to collaborate with
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physiology Language skills (English) Intermediate programming skills (R, Python or Matlab) Creative thinking Level of enthusiasm and motivation As a formal qualification, you must hold a PhD degree (or
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Multiphysics, MATLAB, or Python Familiarity with device-level simulations and multi-physics approaches Self-motivated and able to work independently while contributing to a collaborative research environment
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for predictivemodelling and large foundation models for earth systems. Skillsetssuch as programming in Python/R, Google Earth Engine, JavaScript, database management environments, Geographical AI, and machine learning